From 737ec8aebd8369f687485adfe5698373c0bd80fb Mon Sep 17 00:00:00 2001 From: Matan Perlmutter Date: Tue, 7 Dec 2021 14:31:56 +0200 Subject: [PATCH 1/7] add run wrap --- deepchecks/base/check.py | 27 +- .../checks/distribution/train_test_drift.py | 7 +- .../checks/integrity/data_duplicates.py | 2 +- .../integrity/dominant_frequency_change.py | 2 +- .../checks/integrity/is_single_value.py | 2 +- .../checks/integrity/label_ambiguity.py | 2 +- deepchecks/checks/integrity/mixed_nulls.py | 2 +- deepchecks/checks/integrity/mixed_types.py | 2 +- deepchecks/checks/integrity/new_category.py | 2 +- deepchecks/checks/integrity/new_label.py | 2 +- .../checks/integrity/rare_format_detection.py | 2 +- deepchecks/checks/integrity/special_chars.py | 2 +- .../integrity/string_length_out_of_bounds.py | 2 +- .../checks/integrity/string_mismatch.py | 2 +- .../integrity/string_mismatch_comparison.py | 2 +- .../checks/methodology/boosting_overfit.py | 2 +- .../date_train_test_leakage_duplicates.py | 3 +- .../date_train_test_leakage_overlap.py | 5 +- .../checks/methodology/identifier_leakage.py | 2 +- .../checks/methodology/index_leakage.py | 3 +- .../methodology/model_inference_time.py | 2 +- .../checks/methodology/performance_overfit.py | 3 +- .../single_feature_contribution.py | 3 +- ...e_feature_contribution_train_validation.py | 2 +- .../methodology/train_test_samples_mix.py | 2 +- .../checks/methodology/unused_features.py | 2 +- deepchecks/checks/overview/columns_info.py | 2 +- deepchecks/checks/overview/dataset_info.py | 2 +- deepchecks/checks/overview/model_info.py | 2 +- .../checks/performance/calibration_metric.py | 2 +- .../performance/confusion_matrix_report.py | 2 +- .../checks/performance/performance_report.py | 2 +- deepchecks/checks/performance/roc_report.py | 2 +- .../checks/performance/segment_performance.py | 2 +- .../performance/simple_model_comparison.py | 5 +- examples/drafts/checks_demonstration.ipynb | 1495 ----------------- examples/drafts/condition_demo.ipynb | 769 --------- examples/drafts/date_leakage.ipynb | 455 ----- examples/drafts/suite_demonstration.ipynb | 340 ---- 39 files changed, 56 insertions(+), 3113 deletions(-) delete mode 100644 examples/drafts/checks_demonstration.ipynb delete mode 100644 examples/drafts/condition_demo.ipynb delete mode 100644 examples/drafts/date_leakage.ipynb delete mode 100644 examples/drafts/suite_demonstration.ipynb diff --git a/deepchecks/base/check.py b/deepchecks/base/check.py index 543490adf3..329280ab64 100644 --- a/deepchecks/base/check.py +++ b/deepchecks/base/check.py @@ -2,8 +2,10 @@ import abc import enum import re +import typing from collections import OrderedDict from dataclasses import dataclass +from functools import wraps from typing import Any, Callable, List, Union, Dict, cast __all__ = ['CheckResult', 'BaseCheck', 'SingleDatasetBaseCheck', 'CompareDatasetsBaseCheck', 'TrainTestBaseCheck', @@ -118,8 +120,9 @@ class CheckResult: header: str display: List[Union[Callable, str, pd.DataFrame, Styler]] condition_results: List[ConditionResult] + check: typing.ClassVar - def __init__(self, value, header: str = None, check=None, display: Any = None): + def __init__(self, value, header: str = None, display: Any = None): """Init check result. Args: @@ -130,8 +133,7 @@ def __init__(self, value, header: str = None, check=None, display: Any = None): display (List): Objects to be displayed (dataframe or function or html) """ self.value = value - self.header = header or (check and check.name()) or None - self.check = check + self.header = header self.condition_results = [] if display is not None and not isinstance(display, List): @@ -144,10 +146,10 @@ def __init__(self, value, header: str = None, check=None, display: Any = None): raise DeepchecksValueError(f'Can\'t display item of type: {type(item)}') def _ipython_display_(self): - if self.header: - display_html(f'

{self.header}

', raw=True) - if self.check and '__doc__' in dir(self.check): - docs = self.check.__doc__ + header = self.header or self.check.name() + display_html(f'

{header}

', raw=True) + if hasattr(self.check, '__doc__'): + docs = self.check.__doc__ or '' # Take first non-whitespace line. summary = next((s for s in docs.split('\n') if not re.match('^\\s*$', s)), '') display_html(f'

{summary}

', raw=True) @@ -190,6 +192,16 @@ def get_conditions_sort_value(self): return max([r.get_sort_value() for r in self.conditions_results]) +def wrap_run(func, clazz_instance): + """Wrap the run function of checks, and sets the `check` property on the check result.""" + @wraps(func) + def wrapped(*args, **kwargs): + result = func(*args, **kwargs) + result.check = clazz_instance.__class__ + return result + return wrapped + + class BaseCheck(metaclass=abc.ABCMeta): """Base class for check.""" @@ -199,6 +211,7 @@ class BaseCheck(metaclass=abc.ABCMeta): def __init__(self): self._conditions = OrderedDict() self._conditions_index = 0 + setattr(self, 'run', wrap_run(getattr(self, 'run'), self)) def conditions_decision(self, result: CheckResult) -> List[ConditionResult]: """Run conditions on given result.""" diff --git a/deepchecks/checks/distribution/train_test_drift.py b/deepchecks/checks/distribution/train_test_drift.py index 59563d6df2..f6bee2bf52 100644 --- a/deepchecks/checks/distribution/train_test_drift.py +++ b/deepchecks/checks/distribution/train_test_drift.py @@ -233,12 +233,7 @@ def _calc_drift(self, train_dataset: Dataset, test_dataset: Dataset, feature_imp displays = [headnote] + [displays_dict[col] for col in columns_order] - return CheckResult( - value=values_dict, - display=displays, - header='Train Test Drift', - check=self.__class__ - ) + return CheckResult(value=values_dict, display=displays, header='Train Test Drift') def _calc_drift_per_column(self, train_column: pd.Series, test_column: pd.Series, column_name: str, column_type: str, feature_importances: pd.Series = None diff --git a/deepchecks/checks/integrity/data_duplicates.py b/deepchecks/checks/integrity/data_duplicates.py index cbd2990c29..58ef440608 100644 --- a/deepchecks/checks/integrity/data_duplicates.py +++ b/deepchecks/checks/integrity/data_duplicates.py @@ -67,7 +67,7 @@ def run(self, dataset: Dataset, model=None) -> CheckResult: else: display = None - return CheckResult(value=percent_duplicate, check=self.__class__, display=display) + return CheckResult(value=percent_duplicate, display=display) def add_condition_ratio_not_greater_than(self, max_ratio: float = 0): """Add condition - require duplicate ratio to not surpass max_ratio. diff --git a/deepchecks/checks/integrity/dominant_frequency_change.py b/deepchecks/checks/integrity/dominant_frequency_change.py index cdf8326859..177431f1e0 100644 --- a/deepchecks/checks/integrity/dominant_frequency_change.py +++ b/deepchecks/checks/integrity/dominant_frequency_change.py @@ -147,7 +147,7 @@ def _dominant_frequency_change(self, dataset: Dataset, baseline_dataset: Dataset else: sorted_p_df = None - return CheckResult(p_dict, check=self.__class__, display=sorted_p_df) + return CheckResult(p_dict, display=sorted_p_df) def add_condition_p_value_not_less_than(self, p_value_threshold: float = 0.0001): """Add condition - require min p value allowed per column. diff --git a/deepchecks/checks/integrity/is_single_value.py b/deepchecks/checks/integrity/is_single_value.py index 8ce2b222f0..0670537100 100644 --- a/deepchecks/checks/integrity/is_single_value.py +++ b/deepchecks/checks/integrity/is_single_value.py @@ -56,7 +56,7 @@ def _is_single_value(self, dataset: Union[pd.DataFrame, Dataset]) -> CheckResult value = None display = None - return CheckResult(value, header='Single Value in Column', check=self.__class__, display=display) + return CheckResult(value, header='Single Value in Column', display=display) def add_condition_not_single_value(self): """Add condition - not single value.""" diff --git a/deepchecks/checks/integrity/label_ambiguity.py b/deepchecks/checks/integrity/label_ambiguity.py index 3e13ef8df7..41b72106ea 100644 --- a/deepchecks/checks/integrity/label_ambiguity.py +++ b/deepchecks/checks/integrity/label_ambiguity.py @@ -69,7 +69,7 @@ def run(self, dataset: Dataset, model=None) -> CheckResult: percent_ambiguous = num_ambiguous/dataset.n_samples() - return CheckResult(value=percent_ambiguous, check=self.__class__, display=display) + return CheckResult(value=percent_ambiguous, display=display) def add_condition_ambiguous_sample_ratio_not_greater_than(self, max_ratio=0): """Add condition - require samples with multiple labels to not be more than max_ratio. diff --git a/deepchecks/checks/integrity/mixed_nulls.py b/deepchecks/checks/integrity/mixed_nulls.py index 64d97e8d2f..9d7e955dc4 100644 --- a/deepchecks/checks/integrity/mixed_nulls.py +++ b/deepchecks/checks/integrity/mixed_nulls.py @@ -131,7 +131,7 @@ def _mixed_nulls(self, dataset: Union[pd.DataFrame, Dataset], feature_importance else: display = None - return CheckResult(result_dict, check=self.__class__, display=display) + return CheckResult(result_dict, display=display) def add_condition_different_nulls_not_more_than(self, max_allowed_null_types: int = 1): """Add condition - require column not to have more than given number of different null values. diff --git a/deepchecks/checks/integrity/mixed_types.py b/deepchecks/checks/integrity/mixed_types.py index 3e52899641..d6d8f27699 100644 --- a/deepchecks/checks/integrity/mixed_types.py +++ b/deepchecks/checks/integrity/mixed_types.py @@ -81,7 +81,7 @@ def _mixed_types(self, dataset: Union[pd.DataFrame, Dataset], feature_importance else: display = None - return CheckResult(result_dict, check=self.__class__, display=display) + return CheckResult(result_dict, display=display) def _get_data_mix(self, column_data: pd.Series) -> dict: if is_string_column(column_data): diff --git a/deepchecks/checks/integrity/new_category.py b/deepchecks/checks/integrity/new_category.py index 6439f3abd6..d85232c378 100644 --- a/deepchecks/checks/integrity/new_category.py +++ b/deepchecks/checks/integrity/new_category.py @@ -108,7 +108,7 @@ def _new_category_train_test(self, train_dataset: Dataset, test_dataset: Dataset else: display = None new_categories = {} - return CheckResult(new_categories, check=self.__class__, display=display) + return CheckResult(new_categories, display=display) def add_condition_new_categories_not_greater_than(self, max_new: int = 0): """Add condition - require column not to have greater than given number of different new categories. diff --git a/deepchecks/checks/integrity/new_label.py b/deepchecks/checks/integrity/new_label.py index 517227f2f6..2bda6c446c 100644 --- a/deepchecks/checks/integrity/new_label.py +++ b/deepchecks/checks/integrity/new_label.py @@ -70,7 +70,7 @@ def _new_label_train_test(self, train_dataset: Dataset, test_dataset: Dataset): display = None result = {} - return CheckResult(result, check=self.__class__, display=display) + return CheckResult(result, display=display) def add_condition_new_labels_not_greater_than(self, max_new: int = 0): """Add condition - require label column not to have greater than given number of different new labels. diff --git a/deepchecks/checks/integrity/rare_format_detection.py b/deepchecks/checks/integrity/rare_format_detection.py index 67d77fe03d..7444adb4d5 100644 --- a/deepchecks/checks/integrity/rare_format_detection.py +++ b/deepchecks/checks/integrity/rare_format_detection.py @@ -363,7 +363,7 @@ def _rare_format_detection(self, dataset: t.Union[Dataset, pd.DataFrame], display.append(f'\n\nColumn {key}:') display.append(value) - return CheckResult(value=filtered_res, header='Rare Format Detection', check=self.__class__, display=display) + return CheckResult(value=filtered_res, header='Rare Format Detection', display=display) def add_condition_ratio_of_rare_formats_not_greater_than(self, var: float = 0): """ diff --git a/deepchecks/checks/integrity/special_chars.py b/deepchecks/checks/integrity/special_chars.py index 47b120c51e..933c95355a 100644 --- a/deepchecks/checks/integrity/special_chars.py +++ b/deepchecks/checks/integrity/special_chars.py @@ -100,7 +100,7 @@ def _special_characters(self, dataset: Union[pd.DataFrame, Dataset], self.n_top_columns, col='Column Name') display = df_graph if len(df_graph) > 0 else None - return CheckResult(result, check=self.__class__, display=display) + return CheckResult(result, display=display) def add_condition_ratio_of_special_characters_not_grater_than(self, max_ratio: float = 0.001): """Add condition - ratio of entirely special character in column. diff --git a/deepchecks/checks/integrity/string_length_out_of_bounds.py b/deepchecks/checks/integrity/string_length_out_of_bounds.py index 09883847e5..ec7cea9126 100644 --- a/deepchecks/checks/integrity/string_length_out_of_bounds.py +++ b/deepchecks/checks/integrity/string_length_out_of_bounds.py @@ -177,7 +177,7 @@ def _string_length_out_of_bounds(self, dataset: Union[pd.DataFrame, Dataset], self.n_top_columns, col='Column Name') display = df_graph if len(df_graph) > 0 else None - return CheckResult(results, check=self.__class__, display=display) + return CheckResult(results, display=display) def add_condition_number_of_outliers_not_greater_than(self, max_outliers: int = 0): """Add condition - require column not to have more than given number of string length outliers. diff --git a/deepchecks/checks/integrity/string_mismatch.py b/deepchecks/checks/integrity/string_mismatch.py index 06d0e994eb..3702a9cdcb 100644 --- a/deepchecks/checks/integrity/string_mismatch.py +++ b/deepchecks/checks/integrity/string_mismatch.py @@ -104,7 +104,7 @@ def _string_mismatch(self, dataset: Union[pd.DataFrame, Dataset], else: display = None - return CheckResult(result_dict, check=self.__class__, display=display) + return CheckResult(result_dict, display=display) def add_condition_not_more_variants_than(self, num_max_variants: int): """Add condition - no more than given number of variants are allowed (per string baseform). diff --git a/deepchecks/checks/integrity/string_mismatch_comparison.py b/deepchecks/checks/integrity/string_mismatch_comparison.py index 1921bb8845..6ee81ce2d3 100644 --- a/deepchecks/checks/integrity/string_mismatch_comparison.py +++ b/deepchecks/checks/integrity/string_mismatch_comparison.py @@ -137,7 +137,7 @@ def _string_mismatch_comparison(self, dataset: Union[pd.DataFrame, Dataset], else: display = None - return CheckResult(result_dict, check=self.__class__, display=display) + return CheckResult(result_dict, display=display) def add_condition_no_new_variants(self): """Add condition - no new variants allowed in test data.""" diff --git a/deepchecks/checks/methodology/boosting_overfit.py b/deepchecks/checks/methodology/boosting_overfit.py index 9b716e4c37..6d8746c90d 100644 --- a/deepchecks/checks/methodology/boosting_overfit.py +++ b/deepchecks/checks/methodology/boosting_overfit.py @@ -175,7 +175,7 @@ def display_func(): axes.xaxis.set_major_locator(MaxNLocator(integer=True)) result = {'test': test_scores, 'train': train_scores} - return CheckResult(result, check=self.__class__, display=display_func, header='Boosting Overfit') + return CheckResult(result, display=display_func, header='Boosting Overfit') def add_condition_test_score_percent_decline_not_greater_than(self, threshold: float = 0.05): """Add condition. diff --git a/deepchecks/checks/methodology/date_train_test_leakage_duplicates.py b/deepchecks/checks/methodology/date_train_test_leakage_duplicates.py index 293f676abb..5e0bc4433f 100644 --- a/deepchecks/checks/methodology/date_train_test_leakage_duplicates.py +++ b/deepchecks/checks/methodology/date_train_test_leakage_duplicates.py @@ -58,8 +58,7 @@ def _date_train_test_leakage_duplicates(self, train_dataset: Dataset, test_datas display = None return_value = 0 - return CheckResult(value=return_value, header='Date Train-Test Leakage (duplicates)', - check=self.__class__, display=display) + return CheckResult(value=return_value, header='Date Train-Test Leakage (duplicates)', display=display) def add_condition_leakage_ratio_not_greater_than(self, max_ratio: float = 0): """Add condition - require leakage ratio to not surpass max_ratio. diff --git a/deepchecks/checks/methodology/date_train_test_leakage_overlap.py b/deepchecks/checks/methodology/date_train_test_leakage_overlap.py index ea17feb289..6814b3987d 100644 --- a/deepchecks/checks/methodology/date_train_test_leakage_overlap.py +++ b/deepchecks/checks/methodology/date_train_test_leakage_overlap.py @@ -48,10 +48,7 @@ def _date_train_test_leakage_overlap(self, train_dataset: Dataset, test_dataset: display = None return_value = 0 - return CheckResult(value=return_value, - header='Date Train-Test Leakage (overlap)', - check=self.__class__, - display=display) + return CheckResult(value=return_value, header='Date Train-Test Leakage (overlap)', display=display) def add_condition_leakage_ratio_not_greater_than(self, max_ratio: float = 0): """Add condition - require leakage ratio to not surpass max_ratio. diff --git a/deepchecks/checks/methodology/identifier_leakage.py b/deepchecks/checks/methodology/identifier_leakage.py index 61efc03c62..a0b73f5ef8 100644 --- a/deepchecks/checks/methodology/identifier_leakage.py +++ b/deepchecks/checks/methodology/identifier_leakage.py @@ -68,7 +68,7 @@ def plot(): 'For Identifier columns (Index/Date) PPS should be nearly 0, otherwise date and index have some ' 'predictive effect on the label.'] - return CheckResult(value=s_ppscore.to_dict(), display=[plot, *text], check=self.__class__) + return CheckResult(value=s_ppscore.to_dict(), display=[plot, *text]) def add_condition_pps_not_greater_than(self, max_pps: float = 0): """Add condition - require columns not to have a greater pps than given max. diff --git a/deepchecks/checks/methodology/index_leakage.py b/deepchecks/checks/methodology/index_leakage.py index d7d9c1a3f9..1c27123cb0 100644 --- a/deepchecks/checks/methodology/index_leakage.py +++ b/deepchecks/checks/methodology/index_leakage.py @@ -58,8 +58,7 @@ def _index_train_test_leakage(self, train_dataset: Dataset, test_dataset: Datase size_in_test = 0 display = None - return CheckResult(value=size_in_test, header='Index Train-Test Leakage', check=self.__class__, - display=display) + return CheckResult(value=size_in_test, header='Index Train-Test Leakage', display=display) def add_condition_ratio_not_greater_than(self, max_ratio: float = 0): """Add condition - require index leakage ratio to not surpass max_ratio. diff --git a/deepchecks/checks/methodology/model_inference_time.py b/deepchecks/checks/methodology/model_inference_time.py index 61a7925b12..f608414b8a 100644 --- a/deepchecks/checks/methodology/model_inference_time.py +++ b/deepchecks/checks/methodology/model_inference_time.py @@ -74,7 +74,7 @@ def _model_inference_time_check( result = result / number_of_samples - return CheckResult(value=result, check=type(self), display=( + return CheckResult(value=result, display=( 'Average model inference time for one sample (in seconds): ' f'{format_number(result, floating_point=8)}' )) diff --git a/deepchecks/checks/methodology/performance_overfit.py b/deepchecks/checks/methodology/performance_overfit.py index 449564b0b2..4778bc5d30 100644 --- a/deepchecks/checks/methodology/performance_overfit.py +++ b/deepchecks/checks/methodology/performance_overfit.py @@ -97,8 +97,7 @@ def plot_overfit(): plt.xticks(rotation=30) plt.legend(res_df.columns, loc='upper right', bbox_to_anchor=(1.45, 1.02)) - return CheckResult(result, check=self.__class__, header='Train-Test Difference Overfit', - display=[plot_overfit]) + return CheckResult(result, header='Train-Test Difference Overfit', display=[plot_overfit]) def add_condition_difference_not_greater_than(self: TD, threshold: float) -> TD: """ diff --git a/deepchecks/checks/methodology/single_feature_contribution.py b/deepchecks/checks/methodology/single_feature_contribution.py index 67c6d7fa01..ad5c860049 100644 --- a/deepchecks/checks/methodology/single_feature_contribution.py +++ b/deepchecks/checks/methodology/single_feature_contribution.py @@ -68,8 +68,7 @@ def plot(n_show_top=self.n_show_top): ' actually due to data', 'leakage - meaning that the feature holds information that is based on the label to begin with.'] - return CheckResult(value=s_ppscore.to_dict(), display=[plot, *text], check=self.__class__, - header='Single Feature Contribution') + return CheckResult(value=s_ppscore.to_dict(), display=[plot, *text], header='Single Feature Contribution') def add_condition_feature_pps_not_greater_than(self: FC, threshold: float = 0.8) -> FC: """ diff --git a/deepchecks/checks/methodology/single_feature_contribution_train_validation.py b/deepchecks/checks/methodology/single_feature_contribution_train_validation.py index 57b33588ba..6c629b4247 100644 --- a/deepchecks/checks/methodology/single_feature_contribution_train_validation.py +++ b/deepchecks/checks/methodology/single_feature_contribution_train_validation.py @@ -90,7 +90,7 @@ def plot(): 'that was powerful in train but not in test can be explained by leakage in train that is not ' 'relevant to a new dataset.'] - return CheckResult(value=s_difference.to_dict(), display=[plot, *text], check=self.__class__, + return CheckResult(value=s_difference.to_dict(), display=[plot, *text], header='Single Feature Contribution Train-Test') def add_condition_feature_pps_difference_not_greater_than(self: FC, threshold: float = 0.2) -> FC: diff --git a/deepchecks/checks/methodology/train_test_samples_mix.py b/deepchecks/checks/methodology/train_test_samples_mix.py index 0a5977be3a..5e84ee63d2 100644 --- a/deepchecks/checks/methodology/train_test_samples_mix.py +++ b/deepchecks/checks/methodology/train_test_samples_mix.py @@ -119,7 +119,7 @@ def _data_sample_leakage_report(self, test_dataset: Dataset, train_dataset: Data of test data samples appear in train data' display = [user_msg, duplicate_rows_df.head(10)] if dup_ratio else None - return CheckResult(dup_ratio, header='Train Test Samples Mix', check=self.__class__, display=display) + return CheckResult(dup_ratio, header='Train Test Samples Mix', display=display) def add_condition_duplicates_ratio_not_greater_than(self, max_ratio: float = 0.1): """Add condition - require max allowed ratio of test data samples to appear in train data. diff --git a/deepchecks/checks/methodology/unused_features.py b/deepchecks/checks/methodology/unused_features.py index 1addd5e537..afd2bade5d 100644 --- a/deepchecks/checks/methodology/unused_features.py +++ b/deepchecks/checks/methodology/unused_features.py @@ -200,7 +200,7 @@ def plot_feature_importance(): last_variable_feature_index:].values.tolist() }} - return CheckResult(return_value, check=self.__class__, header='Unused Features', display=display_list) + return CheckResult(return_value, header='Unused Features', display=display_list) def add_condition_number_of_high_variance_unused_features_not_greater_than( self, max_high_variance_unused_features: int = 5): diff --git a/deepchecks/checks/overview/columns_info.py b/deepchecks/checks/overview/columns_info.py index a22d4d47d7..46ea659fcb 100644 --- a/deepchecks/checks/overview/columns_info.py +++ b/deepchecks/checks/overview/columns_info.py @@ -41,5 +41,5 @@ def _columns_info(self, dataset: Dataset, feature_importances: pd.Series=None): df = pd.DataFrame.from_dict(value, orient='index', columns=['role']) df = df.transpose() - return CheckResult(value, check=self.__class__, header='Columns Info', display=df) + return CheckResult(value, header='Columns Info', display=df) diff --git a/deepchecks/checks/overview/dataset_info.py b/deepchecks/checks/overview/dataset_info.py index 0aa39e01bd..3a2af43eef 100644 --- a/deepchecks/checks/overview/dataset_info.py +++ b/deepchecks/checks/overview/dataset_info.py @@ -40,4 +40,4 @@ def display(): profile = ProfileReport(dataset, title='Dataset Report', explorative=True, minimal=True) profile.to_notebook_iframe() - return CheckResult(dataset.shape, check=self.__class__, display=display) + return CheckResult(dataset.shape, display=display) diff --git a/deepchecks/checks/overview/model_info.py b/deepchecks/checks/overview/model_info.py index a4273d2f38..8afeeabc21 100644 --- a/deepchecks/checks/overview/model_info.py +++ b/deepchecks/checks/overview/model_info.py @@ -48,4 +48,4 @@ def highlight_not_default(data): footnote = '

Colored rows are parameters with non-default values

' display = [f'Model Type: {model_type}', model_param_df, footnote] - return CheckResult(value, check=self.__class__, header='Model Info', display=display) + return CheckResult(value, header='Model Info', display=display) diff --git a/deepchecks/checks/performance/calibration_metric.py b/deepchecks/checks/performance/calibration_metric.py index 8da82650f6..cc3a2ea533 100644 --- a/deepchecks/checks/performance/calibration_metric.py +++ b/deepchecks/checks/performance/calibration_metric.py @@ -73,5 +73,5 @@ def display(): "frequency of the positive label against its predicted probability, for binned predictions." brier_text = "The Brier score metric may be used to assess how well a classifier is calibrated. For more " \ "info, please visit https://en.wikipedia.org/wiki/Brier_score" - return CheckResult(briers_scores, header="Calibration Metric", check=self.__class__, + return CheckResult(briers_scores, header="Calibration Metric", display=[calibration_text, display, brier_text]) diff --git a/deepchecks/checks/performance/confusion_matrix_report.py b/deepchecks/checks/performance/confusion_matrix_report.py index 6d4e543efd..8e03b8f15a 100644 --- a/deepchecks/checks/performance/confusion_matrix_report.py +++ b/deepchecks/checks/performance/confusion_matrix_report.py @@ -43,5 +43,5 @@ def _confusion_matrix_report(self, dataset: Dataset, model): def display(): sklearn.metrics.ConfusionMatrixDisplay(confusion_matrix).plot() - return CheckResult(confusion_matrix, check=self.__class__, display=display) + return CheckResult(confusion_matrix, display=display) diff --git a/deepchecks/checks/performance/performance_report.py b/deepchecks/checks/performance/performance_report.py index c40c860390..84d782fbdd 100644 --- a/deepchecks/checks/performance/performance_report.py +++ b/deepchecks/checks/performance/performance_report.py @@ -46,7 +46,7 @@ def _performance_report(self, dataset: Dataset, model): display_df = pd.DataFrame(scores.values(), columns=['Score'], index=scores.keys()) display_df.index.name = 'Metric' - return CheckResult(scores, check=self.__class__, header='Performance Report', display=display_df) + return CheckResult(scores, header='Performance Report', display=display_df) def add_condition_score_not_less_than(self, min_score: float): """Add condition - metric scores are not less than given score. diff --git a/deepchecks/checks/performance/roc_report.py b/deepchecks/checks/performance/roc_report.py index 4e1359e55c..4dc2607dd2 100644 --- a/deepchecks/checks/performance/roc_report.py +++ b/deepchecks/checks/performance/roc_report.py @@ -87,7 +87,7 @@ def display(): plt.title('ROC curves') plt.legend(loc='lower right') - return CheckResult(roc_auc, header='ROC Report', check=self.__class__, display=display) + return CheckResult(roc_auc, header='ROC Report', display=display) def add_condition_auc_not_less_than(self, min_auc: float = 0.7): """Add condition - require min allowed AUC score per class. diff --git a/deepchecks/checks/performance/segment_performance.py b/deepchecks/checks/performance/segment_performance.py index b10ffcf308..f726bb5ecc 100644 --- a/deepchecks/checks/performance/segment_performance.py +++ b/deepchecks/checks/performance/segment_performance.py @@ -136,4 +136,4 @@ def display(feat1=self.feature_1, feat2=self.feature_2): ax.set_title(f'{metric_name} (count) by features {feat1}/{feat2}') value = {'scores': scores, 'counts': counts, 'feature_1': self.feature_1,'feature_2': self.feature_2} - return CheckResult(value, check=self.__class__, display=display) + return CheckResult(value, display=display) diff --git a/deepchecks/checks/performance/simple_model_comparison.py b/deepchecks/checks/performance/simple_model_comparison.py index 1ccb620b38..bef452589e 100644 --- a/deepchecks/checks/performance/simple_model_comparison.py +++ b/deepchecks/checks/performance/simple_model_comparison.py @@ -165,9 +165,10 @@ def display_func(): ax.bar(models, metrics_results) ax.set_ylabel(metric_name) - return CheckResult({'given_model_score': pred_metric, 'simple_model_score': simple_metric, + return CheckResult({'given_model_score': pred_metric, + 'simple_model_score': simple_metric, 'ratio': ratio}, - check=self.__class__, display=[text, display_func]) + display=[text, display_func]) def add_condition_ratio_not_less_than(self, min_allowed_ratio: float = 1.1): """Add condition - require min allowed ratio between the given and the simple model. diff --git a/examples/drafts/checks_demonstration.ipynb b/examples/drafts/checks_demonstration.ipynb deleted file mode 100644 index e85030ab73..0000000000 --- a/examples/drafts/checks_demonstration.ipynb +++ /dev/null @@ -1,1495 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9f7d1e20", - "metadata": {}, - "source": [ - "## Imports and configurations" - ] - }, - { - "cell_type": "markdown", - "id": "9254315d", - "metadata": {}, - "source": [ - "### Config directory path for demo datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "da391550", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:36.150652Z", - "start_time": "2021-11-02T13:17:36.141249Z" - } - }, - "outputs": [], - "source": [ - "import os" - ] - }, - { - "cell_type": "markdown", - "id": "80c06c83", - "metadata": {}, - "source": [ - "NOTE - Set DATASETS_BASEDIR to your local folder that contains all required datasets.
\n", - "Datasets can be found in shared folder:
https://drive.google.com/drive/u/0/folders/1WIjlwoUdgwrQj1S9UmJLMbJT6NuKeX7t" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5dca81c7", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:36.162982Z", - "start_time": "2021-11-02T13:17:36.158671Z" - } - }, - "outputs": [], - "source": [ - "DATASETS_BASEDIR = '../../../../Datasets'" - ] - }, - { - "cell_type": "markdown", - "id": "1448791a", - "metadata": {}, - "source": [ - "Load dataset paths" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "85ba0400", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:36.176979Z", - "start_time": "2021-11-02T13:17:36.170787Z" - } - }, - "outputs": [], - "source": [ - "# verify that DATASETS_BASEDIR exists a\n", - "dataset_names = os.listdir(DATASETS_BASEDIR)\n", - "# print(dataset_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "47e0e1d3", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:36.189265Z", - "start_time": "2021-11-02T13:17:36.184003Z" - } - }, - "outputs": [], - "source": [ - "# List all datasets used\n", - "DATASET_PATHS = {}\n", - "DATASET_PATHS['Lending_Club'] = os.path.join(DATASETS_BASEDIR, 'Lending Club')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "33c8513c", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:36.202774Z", - "start_time": "2021-11-02T13:17:36.196342Z" - } - }, - "outputs": [], - "source": [ - "for dataset_name in DATASET_PATHS:\n", - " if not os.path.exists(DATASET_PATHS[dataset_name]):\n", - " print(\"Verify that all required datasets are in your datasets folder!\")\n", - " raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), DATASET_PATHS[dataset_name])" - ] - }, - { - "cell_type": "markdown", - "id": "298e795f", - "metadata": {}, - "source": [ - "### General Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c0e6b0d5", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:49.764501Z", - "start_time": "2021-11-02T13:17:41.830342Z" - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import joblib\n", - "import errno" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2ab583b4", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:56.182643Z", - "start_time": "2021-11-02T13:17:49.767136Z" - } - }, - "outputs": [], - "source": [ - "from sklearn.model_selection import train_test_split\n", - "from sklearn.datasets import load_iris\n", - "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier" - ] - }, - { - "cell_type": "markdown", - "id": "205ba279", - "metadata": {}, - "source": [ - "### Imports for checks" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2a0dd36e", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:18:00.007037Z", - "start_time": "2021-11-02T13:17:56.184529Z" - } - }, - "outputs": [], - "source": [ - "import mlchecks\n", - "from mlchecks.base import Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "54ee33da", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:35:39.678398Z", - "start_time": "2021-11-02T13:35:39.673180Z" - } - }, - "outputs": [], - "source": [ - "# Note - all checks are initialized also in mlchecks.checks and can be imported directly from there\n", - "# Demonstration here it is just for the sake of order" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "ab40619e", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:34:54.777509Z", - "start_time": "2021-11-02T13:34:54.772540Z" - }, - "code_folding": [] - }, - "outputs": [], - "source": [ - "# Overview\n", - "from mlchecks.checks.overview import dataset_info, DatasetInfo\n", - "from mlchecks.checks.overview import model_info, ModelInfo\n", - "from mlchecks.checks.overview import feature_importance, FeatureImportance" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "0e2833c5", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T18:52:36.361482Z", - "start_time": "2021-11-02T18:52:36.355208Z" - } - }, - "outputs": [], - "source": [ - "# Integrity\n", - "\n", - "from mlchecks.checks.integrity import data_duplicates, DataDuplicates\n", - "from mlchecks.checks.integrity import dominant_frequency_change, DominantFrequencyChange\n", - "from mlchecks.checks.integrity import is_single_value, IsSingleValue\n", - "from mlchecks.checks.integrity import mixed_nulls, MixedNulls\n", - "from mlchecks.checks.integrity import mixed_types, MixedTypes\n", - "from mlchecks.checks.integrity import new_category_train_validation, CategoryMismatchTrainTest\n", - "from mlchecks.checks.integrity import new_label_train_validation, NewLabelTrainTest\n", - "from mlchecks.checks.integrity import rare_format_detection, RareFormatDetection\n", - "from mlchecks.checks.integrity import special_characters, SpecialCharacters\n", - "from mlchecks.checks.integrity import string_length_outlier, StringLengthOutlier\n", - "from mlchecks.checks.integrity import string_mismatch, StringMismatch\n", - "from mlchecks.checks.integrity import string_mismatch_comparison, StringMismatchComparison" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8836ac5", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:34:19.900597Z", - "start_time": "2021-11-02T13:34:19.894115Z" - } - }, - "outputs": [], - "source": [ - "# Leakage\n", - "\n", - "from mlchecks.checks.leakage import data_sample_leakage_report, DataSampleLeakageReport\n", - "\n", - "from mlchecks.checks.leakage import date_train_validation_leakage_overlap, DateTrainTestLeakageOverlap\n", - "from mlchecks.checks.leakage import date_train_validation_leakage_duplicates, DateTrainTestLeakageDuplicates\n", - "\n", - "from mlchecks.checks.leage import single_feature_contribution, SingleFeatureContribution\n", - "from mlchecks.checks.leage import single_feature_contribution_train_validation, SingleFeatureContributionTrainTest\n", - "\n", - "from mlchecks.checks.leakage import index_train_validation_leakage, IndexTrainTestLeakage" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "2161be95", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:34:19.900597Z", - "start_time": "2021-11-02T13:34:19.894115Z" - } - }, - "outputs": [], - "source": [ - "from mlchecks.checks.performance import performance_report, confusion_matrix_report, PerformanceReport, ConfusionMatrixReport" - ] - }, - { - "cell_type": "markdown", - "id": "17e6b95d", - "metadata": {}, - "source": [ - "#### Leakage" - ] - }, - { - "cell_type": "markdown", - "id": "b75e4443", - "metadata": {}, - "source": [ - "#### " - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "470f7e49", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:31:59.032058Z", - "start_time": "2021-11-02T13:31:46.683569Z" - } - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'BoostingOverfit' from 'mlchecks.checks.overfit' (/mnt/c/Users/Shir/NoSync_Documents/Git/MLChecks/mlchecks/checks/overfit/__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_8883/810330316.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Checks that were in demo but aren't in master yet:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrare_format_detection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRareFormatDetection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrare_format_detection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverfit\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mboosting_overfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBoostingOverfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverfit\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mperformance_overfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerformanceOverfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_drift\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdataset_drift\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'BoostingOverfit' from 'mlchecks.checks.overfit' (/mnt/c/Users/Shir/NoSync_Documents/Git/MLChecks/mlchecks/checks/overfit/__init__.py)" - ] - } - ], - "source": [ - "# additiona\n", - "from mlchecks.checks.integrity.rare_format_detection import RareFormatDetection, rare_format_detection\n", - "from mlchecks.checks.overfit import boosting_overfit, BoostingOverfit\n", - "from mlchecks.checks.overfit import performance_overfit, PerformanceOverfit\n", - "from mlchecks.checks.integrity.dataset_drift import dataset_drift" - ] - }, - { - "cell_type": "markdown", - "id": "facdec56", - "metadata": {}, - "source": [ - "## Lending Club" - ] - }, - { - "cell_type": "markdown", - "id": "3522957a", - "metadata": {}, - "source": [ - "### Load Data & Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08ecb914", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:16:25.582726Z", - "start_time": "2021-11-02T13:16:22.526799Z" - } - }, - "outputs": [], - "source": [ - "lending_club_path = DATASET_PATHS['Lending_Club']\n", - "df_train_lending_club = pd.read_csv(os.path.join(lending_club_path, 'train.csv'))\n", - "df_train_lending_club.issue_d = pd.to_datetime(df_train.issue_d)\n", - "df_val_lending_club = pd.read_csv(os.path.join(lending_club_path, 'test.csv'))\n", - "df_val.issue_d = pd.to_datetime(df_val.issue_d)\n", - "lending_club_catboost_clf = joblib.load(os.path.join(lending_club_path, 'model.joblib'))" - ] - }, - { - "cell_type": "markdown", - "id": "6650378f", - "metadata": {}, - "source": [ - "#### Define Metadata for Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c884cbd4", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:52.273738Z", - "start_time": "2021-11-01T12:29:52.268488Z" - } - }, - "outputs": [], - "source": [ - "# dataset metadata (manaul)\n", - "\n", - "categorical_features = ['addr_state',\n", - " 'application_type',\n", - "# 'disbursement_method',\n", - "# 'grade',\n", - " 'home_ownership',\n", - " 'initial_list_status',\n", - " 'purpose',\n", - " 'term',\n", - " 'verification_status']\n", - "\n", - "all_features = ['sub_grade', 'term', 'home_ownership', 'fico_range_low',\n", - " 'total_acc', 'pub_rec', 'revol_util', 'annual_inc', 'int_rate', 'dti',\n", - " 'purpose', 'mort_acc', 'loan_amnt', 'application_type', 'installment',\n", - " 'verification_status', 'pub_rec_bankruptcies', 'addr_state',\n", - " 'initial_list_status', 'fico_range_high', 'revol_bal', 'open_acc',\n", - " 'emp_length', 'time_to_earliest_cr_line']\n", - "\n", - "label_col_name = 'loan_status'\n", - "index_col_name = 'id'\n", - "date_col_name = 'issue_d'\n", - "# label_name_dict = {0: \"Default\", 1: \"OK\"}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f6d3c070", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:52.721742Z", - "start_time": "2021-11-01T12:29:52.712161Z" - } - }, - "outputs": [], - "source": [ - "df_train_lending_club.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2fee781b", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:17:08.205962Z", - "start_time": "2021-11-02T13:17:05.888123Z" - } - }, - "outputs": [], - "source": [ - "ds_train_lending_club = Dataset(df_train_lending_club, cat_features = categorical_features, features=all_features,\n", - " label = label_col_name, index = index_col_name, date=date_col_name)\n", - "ds_val_lending_club = Dataset(df_val_lending_club, cat_features = categorical_features, features=all_features,\n", - " label = label_col_name, index = index_col_name, date=date_col_name)" - ] - }, - { - "cell_type": "markdown", - "id": "869023f2", - "metadata": {}, - "source": [ - "### Additional for showing validation faults\n" - ] - }, - { - "cell_type": "markdown", - "id": "1edcd16a", - "metadata": {}, - "source": [ - "#### demo util function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1dcf3d23", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:55.831236Z", - "start_time": "2021-11-01T12:29:55.826981Z" - } - }, - "outputs": [], - "source": [ - "def dataset_from_dict(d: dict, index_name: str = None) -> Dataset:\n", - " dataframe = pd.DataFrame(data=d)\n", - " return Dataset(dataframe, index=index_name)" - ] - }, - { - "cell_type": "markdown", - "id": "f3fe6164", - "metadata": {}, - "source": [ - "#### demo data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53e23423", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:56.805419Z", - "start_time": "2021-11-01T12:29:56.799279Z" - } - }, - "outputs": [], - "source": [ - "# mixed nulls\n", - "mixed_nulls_demo_data = {'col1': ['nan', None, 'null', 'Nan', '1', 'cat'], 'col2':['', '', 'None', 'a', 'b', 'c'], 'col3': [1,2,3,4,5,6]}\n", - "df_mixed_nulls = pd.DataFrame(data=mixed_nulls_demo_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00ee0294", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:57.278581Z", - "start_time": "2021-11-01T12:29:57.272334Z" - } - }, - "outputs": [], - "source": [ - "# single value\n", - "df_single_value_demo = pd.DataFrame({'a':[3,4,1], 'b':[2,2,2], 'c':[None, None, None], 'd':['a', 4, 6]})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "010bd416", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:57.635045Z", - "start_time": "2021-11-01T12:29:57.624211Z" - } - }, - "outputs": [], - "source": [ - "# synthetic index leakage\n", - "train_df_synthetic_leakage = dataset_from_dict({'col1': [1, 2, 3, 4, 10, 11]}, 'col1')\n", - "val_df_synthetic_leakage = dataset_from_dict({'col1': [4, 3, 5, 6, 7]}, 'col1')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5181e28", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:57.965809Z", - "start_time": "2021-11-01T12:29:57.958939Z" - } - }, - "outputs": [], - "source": [ - "# string mismatch data\n", - "data = {'col1': ['Deep', 'deep', 'deep!!!', '$deeP$', 'earth', 'foo', 'bar', 'foo?']}\n", - "df_string_mismatch = pd.DataFrame(data=data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8a0a254", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:58.396481Z", - "start_time": "2021-11-01T12:29:58.377096Z" - } - }, - "outputs": [], - "source": [ - "# index leakage\n", - "iris = load_iris(as_frame=True)\n", - "frame = iris.frame\n", - "X = iris.data\n", - "y = iris.target\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=55)\n", - "train_ds_index_leakage = Dataset(pd.concat([X_train, y_train], axis=1), \n", - " features=iris.feature_names,\n", - " label='target')\n", - "\n", - "test_df = pd.concat([X_test, y_test], axis=1)\n", - "bad_test = test_df.append(train_ds_index_leakage.data.iloc[[0, 1, 2, 3, 4]], ignore_index=True)\n", - " \n", - "val_ds_index_leakage = Dataset(bad_test, \n", - " features=iris.feature_names,\n", - " label='target')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dc69a19a", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:29:58.987167Z", - "start_time": "2021-11-01T12:29:58.966313Z" - } - }, - "outputs": [], - "source": [ - "# rare format detection\n", - "df = pd.DataFrame(np.random.choice(a=['BIG', 'STILL_BIG'], size=(200,3)), columns=['x1', 'x2', 'x3'])\n", - "df = df.append({'x1': 'bla', 'x2': 'BIG', 'x3': 1}, ignore_index=True)\n", - "df = df.append({'x1': 'bla', 'x2': 'BIG', 'x3': 1}, ignore_index=True)\n", - "rare_format_df = df.append({'x1': 'bla2', 'x2': 'BIG', 'x3': 2}, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "71254dc0", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:03:03.809278Z", - "start_time": "2021-11-02T13:03:03.646330Z" - } - }, - "outputs": [], - "source": [ - "# multiclass models - adaboost, randomforest (e.g. for overfit check)\n", - "iris = load_iris(as_frame=True)\n", - "frame = iris.frame\n", - "X_iris = iris.data\n", - "Y_iris = iris.target\n", - "X_train_iris, X_test_iris, y_train_iris, y_test_iris = train_test_split(\n", - " X, Y, test_size=0.33, random_state=42)\n", - "ds_train_iris = Dataset(pd.concat([X_train_iris, y_train_iris], axis=1), \n", - " features=iris.feature_names,\n", - " label='target')\n", - "ds_val_iris = Dataset(pd.concat([X_test_iris, y_test_iris], axis=1), \n", - " features=iris.feature_names,\n", - " label='target')\n", - "iris_multiclass_adaboost_clf = AdaBoostClassifier()\n", - "iris_multiclass_adaboost_clf.fit(ds_train_iris.data.drop(ds_train_iris.label_name(), axis=1), ds_train_iris.label_col())\n", - "iris_multiclass_rf_clf = RandomForestClassifier()\n", - "iris_multiclass_rf_clf.fit(ds_train_iris.data.drop(ds_train_iris.label_name(), axis=1), ds_train_iris.label_col())" - ] - }, - { - "cell_type": "markdown", - "id": "73462329", - "metadata": {}, - "source": [ - "##### Drift demo data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ed46afa8", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:00.314390Z", - "start_time": "2021-11-01T12:30:00.307216Z" - } - }, - "outputs": [], - "source": [ - "# Commented out all this cause drift feature isn't in master yet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81bb9cd3", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:00.570780Z", - "start_time": "2021-11-01T12:30:00.565844Z" - } - }, - "outputs": [], - "source": [ - "# df = pd.read_csv(os.path.join(KKBOX_DATASET_BASEDIR, 'train_clean.csv'))\n", - "# test_df = pd.read_csv(os.path.join(KKBOX_DATASET_BASEDIR, 'test_clean.csv'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e43a4f39", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:00.726259Z", - "start_time": "2021-11-01T12:30:00.722716Z" - } - }, - "outputs": [], - "source": [ - "# test_df.date = pd.to_datetime(test_df.date*10**9)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d81fe7b7", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:00.916335Z", - "start_time": "2021-11-01T12:30:00.910552Z" - } - }, - "outputs": [], - "source": [ - "# drift_org_dataset = Dataset(test_df, \n", - "# features=['num_25', 'num_50', 'num_75', 'num_985', 'num_100', 'num_unq',\n", - "# 'total_secs', 'days_listened', 'plan_list_price', 'is_auto_renew',\n", - "# 'is_cancel', 'gender', 'registered_via', 'secs_per_song', 'num_days'],\n", - "# label='y_true49a0c676-35fd-11ea-978f-2e728ce88125',\n", - "# cat_features= ['gender', 'registered_via'],\n", - "# index='msno', date='date')\n", - "\n", - "# drift_compared_dataset = Dataset(df,\n", - "# features=['num_25', 'num_50', 'num_75', 'num_985', 'num_100', 'num_unq',\n", - "# 'total_secs', 'days_listened', 'plan_list_price', 'is_auto_renew',\n", - "# 'is_cancel', 'gender', 'registered_via', 'secs_per_song', 'num_days'],\n", - "# label='y_true49a0c676-35fd-11ea-978f-2e728ce88125',\n", - "# cat_features= ['gender', 'registered_via'],\n", - "# index='msno')" - ] - }, - { - "cell_type": "markdown", - "id": "316aceb1", - "metadata": {}, - "source": [ - "## Run checks" - ] - }, - { - "cell_type": "markdown", - "id": "83245c45", - "metadata": {}, - "source": [ - "### Overview" - ] - }, - { - "cell_type": "markdown", - "id": "6f254965", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### Dataset Info" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c2dbc02c", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.312426Z", - "start_time": "2021-11-01T12:30:01.515239Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "dataset_info(ds_train)" - ] - }, - { - "cell_type": "markdown", - "id": "5145a944", - "metadata": {}, - "source": [ - "#### Feature Importance (SHAP)" - ] - }, - { - "cell_type": "markdown", - "id": "8485039b", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "##### Binary Classifier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b861b71a", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:14:55.938309Z", - "start_time": "2021-11-02T13:14:55.928234Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "ds_train.data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc9a1901", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:14:31.929720Z", - "start_time": "2021-11-02T13:13:25.516894Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "feature_importance(ds_train, lending_club_model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd4e5cda", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:13:19.347212Z", - "start_time": "2021-11-02T13:13:19.347182Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "res" - ] - }, - { - "cell_type": "markdown", - "id": "4114c894", - "metadata": {}, - "source": [ - "##### Multi-class Classifier" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6d048f5", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:13:22.280987Z", - "start_time": "2021-11-02T13:13:21.432169Z" - } - }, - "outputs": [], - "source": [ - "feature_importance(ds_train_iris, iris_multiclass_rf_clf)" - ] - }, - { - "cell_type": "markdown", - "id": "29f97a59", - "metadata": {}, - "source": [ - "#### Model Info" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a65f5ff7", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:03:22.273598Z", - "start_time": "2021-11-02T13:03:22.230310Z" - } - }, - "outputs": [], - "source": [ - "model_info(lending_club_model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "03520051", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-02T13:03:41.985923Z", - "start_time": "2021-11-02T13:03:41.971434Z" - } - }, - "outputs": [], - "source": [ - "model_info(iris_multiclass_adaboost_clf)" - ] - }, - { - "cell_type": "markdown", - "id": "cc05c824", - "metadata": {}, - "source": [ - "### Integrity" - ] - }, - { - "cell_type": "markdown", - "id": "53edb405", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### Mixed Nulls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87f0e080", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.471141Z", - "start_time": "2021-11-01T12:30:20.455225Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "mixed_nulls(df_mixed_nulls)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d6cf1f72", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.591320Z", - "start_time": "2021-11-01T12:30:20.474061Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "mixed_nulls(df_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66598aa4", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.650286Z", - "start_time": "2021-11-01T12:30:20.592816Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "mixed_nulls(df_val)" - ] - }, - { - "cell_type": "markdown", - "id": "1f4debad", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### Single Value" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1fe64c4b", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.663837Z", - "start_time": "2021-11-01T12:30:20.651838Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "is_single_value(df_single_value_demo)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "037539e1", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.797905Z", - "start_time": "2021-11-01T12:30:20.665716Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "is_single_value(df_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2abf8d44", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.866441Z", - "start_time": "2021-11-01T12:30:20.799304Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "is_single_value(df_val)" - ] - }, - { - "cell_type": "markdown", - "id": "8c8eec86", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### String Mismatch - till here done but not updated" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "16ca6eac", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.889247Z", - "start_time": "2021-11-01T12:30:20.868289Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "df_string_mismatch" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "516353c6", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.904494Z", - "start_time": "2021-11-01T12:30:20.892884Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "string_mismatch(df_string_mismatch)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f9eec6bd", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:20.993699Z", - "start_time": "2021-11-01T12:30:20.905944Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "string_mismatch(df_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b23bb5d", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.040501Z", - "start_time": "2021-11-01T12:30:20.995400Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "string_mismatch(df_val)" - ] - }, - { - "cell_type": "markdown", - "id": "3c864b23", - "metadata": {}, - "source": [ - "#### From here TODO" - ] - }, - { - "cell_type": "markdown", - "id": "20ceaaf7", - "metadata": {}, - "source": [ - "#### Data Duplicates" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe5e08e1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "919eb60e", - "metadata": {}, - "source": [ - "#### Dominant Frequency Change" - ] - }, - { - "cell_type": "markdown", - "id": "e696a7fc", - "metadata": {}, - "source": [ - "#### Mixed Types" - ] - }, - { - "cell_type": "markdown", - "id": "9e5c4f05", - "metadata": {}, - "source": [ - "#### New Category" - ] - }, - { - "cell_type": "markdown", - "id": "e8110579", - "metadata": {}, - "source": [ - "#### New Label" - ] - }, - { - "cell_type": "markdown", - "id": "deaa9c27", - "metadata": {}, - "source": [ - "#### Special Characters" - ] - }, - { - "cell_type": "markdown", - "id": "e7745bab", - "metadata": {}, - "source": [ - "#### String Length Outlier" - ] - }, - { - "cell_type": "markdown", - "id": "51b88d26", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:49:36.033828Z", - "start_time": "2021-11-01T12:49:36.029516Z" - } - }, - "source": [ - "#### String Mismatch Comparison" - ] - }, - { - "cell_type": "markdown", - "id": "cd1e0989", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### Rare Format Detection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c5bd5ea", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.044341Z", - "start_time": "2021-11-01T12:30:21.041751Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "# rare_format_detection(rare_format_df)\n", - "# rare_format_df = df.append({'x1': 'bla2', 'x2': 'BIG', 'x3': 2}, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d96d457", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.050396Z", - "start_time": "2021-11-01T12:30:21.045697Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "# rare_format_detection(df_train)" - ] - }, - { - "cell_type": "markdown", - "id": "e9c5c651", - "metadata": {}, - "source": [ - "### Overfit" - ] - }, - { - "cell_type": "markdown", - "id": "0b7dd6c8", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.062636Z", - "start_time": "2021-11-01T12:30:21.057850Z" - } - }, - "source": [ - "#### TODO - Boosting Overfit" - ] - }, - { - "cell_type": "markdown", - "id": "eb56a773", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.067245Z", - "start_time": "2021-11-01T12:30:21.064362Z" - } - }, - "source": [ - "#### TODO - Performance Overfit" - ] - }, - { - "cell_type": "markdown", - "id": "8e36f433", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "### Drift" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1845afe", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:56:39.791532Z", - "start_time": "2021-11-01T12:56:39.788459Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "# TBD" - ] - }, - { - "cell_type": "markdown", - "id": "31251702", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "### Leakage" - ] - }, - { - "cell_type": "markdown", - "id": "7a63405e", - "metadata": { - "hidden": true - }, - "source": [ - "#### Index Train-Validation Leakage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "229a328f", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.083355Z", - "start_time": "2021-11-01T12:30:21.073620Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "index_train_validation_leakage(train_df_synthetic_leakage, val_df_synthetic_leakage)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1514648", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.137582Z", - "start_time": "2021-11-01T12:30:21.084670Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "index_train_validation_leakage(ds_train, ds_val)" - ] - }, - { - "cell_type": "markdown", - "id": "bd2dbb01", - "metadata": { - "hidden": true - }, - "source": [ - "#### Data Sample Leakage" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "de0c3876", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.161434Z", - "start_time": "2021-11-01T12:30:21.139387Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "data_sample_leakage_report(val_ds_index_leakage, train_ds_index_leakage)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae66f715", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:30:21.165733Z", - "start_time": "2021-11-01T12:30:21.163443Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "# data_sample_leakage_report(ds_val, ds_train)" - ] - }, - { - "cell_type": "markdown", - "id": "5c20ece0", - "metadata": { - "hidden": true - }, - "source": [ - "#### TODO - Single Feature Contribution" - ] - }, - { - "cell_type": "markdown", - "id": "7f9f8da1", - "metadata": {}, - "source": [ - "### Performance" - ] - }, - { - "cell_type": "markdown", - "id": "26192191", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### Performance Report" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c239deb3", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:32:10.072389Z", - "start_time": "2021-11-01T12:32:10.068836Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "cls_report_check = PerformanceReport()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a3342531", - "metadata": { - "ExecuteTime": { - "end_time": "2021-11-01T12:32:13.059242Z", - "start_time": "2021-11-01T12:32:12.501950Z" - }, - "hidden": true - }, - "outputs": [], - "source": [ - "cls_report_check.run(ds_val, lending_club_model)" - ] - }, - { - "cell_type": "markdown", - "id": "78323edf", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### TODO - Confusion Matrix Report" - ] - }, - { - "cell_type": "markdown", - "id": "6b05f207", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### TODO - Naive Comparison" - ] - }, - { - "cell_type": "markdown", - "id": "78a83fab", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "#### TODO - ROC Report" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/drafts/condition_demo.ipynb b/examples/drafts/condition_demo.ipynb deleted file mode 100644 index 6c05ca120d..0000000000 --- a/examples/drafts/condition_demo.ipynb +++ /dev/null @@ -1,769 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "aa401fe6", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "e54802d4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "My Single Suite: [\n", - "\tIsSingleValue\n", - "\tMixedNulls(check_nan=True)\n", - "\t\tConditions:\n", - "\t\t\t0: Not more than 3 different null types for all columns\n", - "\tStringMismatch\n", - "\t\tConditions:\n", - "\t\t\t0: No string variants for all columns\n", - "\tStringMismatch_2\n", - "\t\tConditions:\n", - "\t\t\t0: Not more than 35.00% variants for all columns\n", - "\tMixedTypes\n", - "\t\tConditions:\n", - "\t\t\t0: Rare type ratio is not less than 40.00% of samples in all columns\n", - "\tMixedTypes_2\n", - "\t\tConditions:\n", - "\t\t\t0: Rare type ratio is not less than 10.00% of samples in all columns\n", - "\tRareFormatDetection(patterns=[, , , , , , , ], rarity_threshold=0.05, min_unique_common_ratio=0.01, pattern_match_method=first)\n", - "\t\tConditions:\n", - "\t\t\t0: fail example\n", - "]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlchecks import Suite, ConditionResult, ConditionCategory, Dataset\n", - "from mlchecks.checks import *\n", - "\n", - "def condition_exc(r):\n", - " raise Exception('Failed because I need an example')\n", - "\n", - "data = {\n", - " 'col1': ['', '#@$', 'Nan!', '#nan', ''],\n", - " 'col2': ['gabbay', 'GABBAY!!!', 'is', '...', '?Gabbay?'],\n", - " 'col3': [1, 's', 'a', 'b', 'c'],\n", - " 'col4': ['a', 'a', 'a', 'a', 'a']\n", - "}\n", - "\n", - "dataset = Dataset(pd.DataFrame(data=data))\n", - "suite = Suite('My Single Suite',\n", - " IsSingleValue(),\n", - " MixedNulls().add_condition_different_nulls_not_more_than(3),\n", - " StringMismatch().add_condition_no_variants(),\n", - " StringMismatch().add_condition_ratio_variants_not_more_than(0.35),\n", - " MixedTypes().add_condition_rare_type_ratio_not_less_than(0.4),\n", - " MixedTypes().add_condition_rare_type_ratio_not_less_than(0.1),\n", - " RareFormatDetection().add_condition('fail example', condition_exc)\n", - ")\n", - "suite" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "231d6218", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=7, style=ProgressStyle(bar_color='#9…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

My Single Suite

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Conditions Summary

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StatusCheckConditionMore Info
✖
Mixed Nulls - Validation DatasetNot more than 3 different null types for all columnsFound columns with more than 3 null types: col1
✖
String Mismatch - Validation DatasetNot more than 35.00% variants for all columnsFound columns with variants ratio: {'col1': '100%', 'col2': '60.00%'}
✖
Mixed Types - Validation DatasetRare type ratio is not less than 40.00% of samples in all columnsFound columns with low type ratio: col3
!
String Mismatch - Validation DatasetNo string variants for all columnsFound columns with variants: {'col1': ['', 'nan'], 'col2': ['gabbay']}
✓
Mixed Types - Validation DatasetRare type ratio is not less than 10.00% of samples in all columns
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Other Checks Summary (no conditions defined)

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Check
Single Value in Column - Validation Dataset
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Checks that raised an error during run

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
CheckError
RareFormatDetectionFailed because I need an example
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Checks that didn't pass condition

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Mixed Nulls - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Search for various types of null values in a string column(s), including string representations of null.

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  CountPercent of data
Column NameValue  
col1120.00%
#@$120.00%
Nan!120.00%
#nan120.00%
120.00%
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String Mismatch - Validation Dataset

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Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
  ValueCount% In data
Column NameBase form   
col1120.00%
#@$120.00%
nan#nan120.00%
nan120.00%
nanNan!120.00%
col2gabbaygabbay120.00%
gabbayGABBAY!!!120.00%
gabbay?Gabbay?120.00%
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Mixed Types - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Search for various types of data in (a) column[s], including hidden mixes in strings.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 col3
numbers20.00%
strings80.00%
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

String Mismatch - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
  ValueCount% In data
Column NameBase form   
col1120.00%
#@$120.00%
nan#nan120.00%
nan120.00%
nanNan!120.00%
col2gabbaygabbay120.00%
gabbayGABBAY!!!120.00%
gabbay?Gabbay?120.00%
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Checks without condition

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Single Value in Column - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Check if there are columns which have only a single unique value in all rows.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "The following columns have only one unique value" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 col4
Single unique valuea
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Checks that passed condition

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Mixed Types - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Search for various types of data in (a) column[s], including hidden mixes in strings.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 col3
numbers20.00%
strings80.00%
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result = suite.run(validation_dataset=dataset)\n", - "result\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/drafts/date_leakage.ipynb b/examples/drafts/date_leakage.ipynb deleted file mode 100644 index 84f654e23a..0000000000 --- a/examples/drafts/date_leakage.ipynb +++ /dev/null @@ -1,455 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "7a16525a", - "metadata": {}, - "outputs": [], - "source": [ - "from mlchecks.checks.leakage import DateTrainTestLeakageDuplicates, DateTrainTestLeakageOverlap\n", - "from mlchecks.base import Dataset, Suite\n", - "from datetime import datetime\n", - "import pandas as pd\n", - "\n", - "def dataset_from_dict(d: dict, date_name: str = None) -> Dataset:\n", - " dataframe = pd.DataFrame(data=d)\n", - " return Dataset(dataframe, date=date_name)" - ] - }, - { - "cell_type": "markdown", - "id": "d80ca9b7-dc21-4edc-b8b5-b0186f5ff894", - "metadata": {}, - "source": [ - "## Synthetic example with date leakage" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2e089e04", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=2, style=ProgressStyle(bar_color='#9…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

date leakage

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Checks without condition

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Date Train-Validation Leakage (overlap)

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Check validation data that is dated earlier than latest date in train.

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Date Train-Validation Leakage (duplicates)

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Check if validation dates are present in train data.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "18.2% of validation data dates appear in training data" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 0
Sample of validation dates in train:[Timestamp('2021-10-05 00:00:00')]
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_ds = dataset_from_dict({'col1': [\n", - " datetime(2021, 10, 1, 0, 0),\n", - " datetime(2021, 10, 1, 0, 0),\n", - " datetime(2021, 10, 1, 0, 0),\n", - " datetime(2021, 10, 2, 0, 0),\n", - " datetime(2021, 10, 2, 0, 0),\n", - " datetime(2021, 10, 2, 0, 0),\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 5, 0, 0),\n", - " datetime(2021, 10, 5, 0, 0)\n", - " ]}, 'col1')\n", - "val_ds = dataset_from_dict({'col1': [\n", - " datetime(2021, 9, 4, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 5, 0, 0),\n", - " datetime(2021, 10, 6, 0, 0),\n", - " datetime(2021, 10, 6, 0, 0),\n", - " datetime(2021, 10, 7, 0, 0),\n", - " datetime(2021, 10, 7, 0, 0),\n", - " datetime(2021, 10, 8, 0, 0),\n", - " datetime(2021, 10, 8, 0, 0),\n", - " datetime(2021, 10, 9, 0, 0),\n", - " datetime(2021, 10, 9, 0, 0)\n", - " ]}, 'col1')\n", - "\n", - "suite = Suite(\"date leakage\", DateTrainTestLeakageOverlap(),\n", - " DateTrainTestLeakageDuplicates(n_to_show=1))\n", - "suite.run(train_dataset=train_ds, validation_dataset=val_ds)" - ] - }, - { - "cell_type": "markdown", - "id": "dbf03393", - "metadata": {}, - "source": [ - "## Synthetic example dates before last training" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "16feea0d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=2, style=ProgressStyle(bar_color='#9…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

date leakage

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Checks without condition

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Date Train-Validation Leakage (overlap)

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Check validation data that is dated earlier than latest date in train.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "11.1% of validation data dates before last training data date (2021-10-05 00:00:00)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Checks with nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Check
Date Train-Validation Leakage (duplicates)
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_ds = dataset_from_dict({'col1': [\n", - " datetime(2021, 10, 1, 0, 0),\n", - " datetime(2021, 10, 1, 0, 0),\n", - " datetime(2021, 10, 1, 0, 0),\n", - " datetime(2021, 10, 2, 0, 0),\n", - " datetime(2021, 10, 2, 0, 0),\n", - " datetime(2021, 10, 2, 0, 0),\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 5, 0, 0),\n", - " datetime(2021, 10, 5, 0, 0)\n", - " ]}, 'col1')\n", - "val_ds = dataset_from_dict({'col1': [\n", - " datetime(2021, 9, 4, 0, 0),\n", - " datetime(2021, 10, 6, 0, 0),\n", - " datetime(2021, 10, 6, 0, 0),\n", - " datetime(2021, 10, 7, 0, 0),\n", - " datetime(2021, 10, 7, 0, 0),\n", - " datetime(2021, 10, 8, 0, 0),\n", - " datetime(2021, 10, 8, 0, 0),\n", - " datetime(2021, 10, 9, 0, 0),\n", - " datetime(2021, 10, 9, 0, 0)\n", - " ]}, 'col1')\n", - "\n", - "suite = Suite(\"date leakage\", DateTrainTestLeakageOverlap(),\n", - " DateTrainTestLeakageDuplicates())\n", - "suite.run(train_dataset=train_ds, validation_dataset=val_ds)" - ] - }, - { - "cell_type": "markdown", - "id": "c1da7840", - "metadata": {}, - "source": [ - "## Synthetic example no date leakage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e1c5f401", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=2, style=ProgressStyle(bar_color='#9…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

date leakage

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Checks with nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Check
Date Train-Validation Leakage (overlap)
Date Train-Validation Leakage (duplicates)
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_ds = dataset_from_dict({'col1': [\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 3, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 4, 0, 0),\n", - " datetime(2021, 10, 5, 0, 0),\n", - " datetime(2021, 10, 5, 0, 0)\n", - " ]}, 'col1')\n", - "val_ds = dataset_from_dict({'col1': [\n", - " datetime(2021, 11, 4, 0, 0),\n", - " datetime(2021, 11, 4, 0, 0),\n", - " datetime(2021, 11, 5, 0, 0),\n", - " datetime(2021, 11, 6, 0, 0),\n", - "\n", - " ]}, 'col1')\n", - "\n", - "suite = Suite(\"date leakage\", DateTrainTestLeakageOverlap(),\n", - " DateTrainTestLeakageDuplicates())\n", - "suite.run(train_dataset=train_ds, validation_dataset=val_ds)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/drafts/suite_demonstration.ipynb b/examples/drafts/suite_demonstration.ipynb deleted file mode 100644 index 888ab66e79..0000000000 --- a/examples/drafts/suite_demonstration.ipynb +++ /dev/null @@ -1,340 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "aa401fe6", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e54802d4", - "metadata": {}, - "outputs": [], - "source": [ - "from mlchecks import Suite\n", - "from mlchecks.checks import *\n", - "\n", - "suite = Suite('My Single Suite',\n", - " IsSingleValue(),\n", - " MixedNulls(),\n", - " MixedTypes(),\n", - " StringMismatch()\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "231d6218", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

My Single Suite

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Single Value in Column - Train Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Check if there are columns which have only a single unique value in all rows.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

✓ Nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Single Value in Column - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Check if there are columns which have only a single unique value in all rows.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "The following columns have only one unique value" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 target
Single unique value2
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Mixed Nulls - Train Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Search for various types of null values in a string column(s), including string representations of null.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

✓ Nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Mixed Nulls - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Search for various types of null values in a string column(s), including string representations of null.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

✓ Nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Mixed Types - Train Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Search for mixed types of Data in a single column[s].

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

✓ Nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Mixed Types - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Search for mixed types of Data in a single column[s].

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

✓ Nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

String Mismatch - Train Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

✓ Nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

String Mismatch - Validation Dataset

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "

✓ Nothing found

" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from sklearn.datasets import load_iris\n", - "\n", - "df = load_iris(return_X_y=False, as_frame=True)\n", - "df = pd.concat([df.data, df.target], axis=1)\n", - "\n", - "train = df[:100]\n", - "val = df[100:]\n", - "\n", - "suite.run(train_dataset=train, validation_dataset=val, check_datasets_policy='both')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": {}, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 02c7c3aa3aa960a30417dfe1791a582131000611 Mon Sep 17 00:00:00 2001 From: Matan Perlmutter Date: Tue, 7 Dec 2021 14:32:10 +0200 Subject: [PATCH 2/7] add run wrap --- deepchecks/checks/distribution/trust_score_comparison.py | 3 +-- tests/base/check_test.py | 4 +++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/deepchecks/checks/distribution/trust_score_comparison.py b/deepchecks/checks/distribution/trust_score_comparison.py index 1771b1e8f0..e73cba6daa 100644 --- a/deepchecks/checks/distribution/trust_score_comparison.py +++ b/deepchecks/checks/distribution/trust_score_comparison.py @@ -189,8 +189,7 @@ def filter_quantile(data): '
Top Trust Score Samples
', top_k] result = {'test': np.mean(test_trust_scores), 'train': np.mean(train_trust_scores)} - return CheckResult(result, check=self.__class__, display=display, - header='Trust Score Comparison: Train vs. Test') + return CheckResult(result, display=display, header='Trust Score Comparison: Train vs. Test') def add_condition_mean_score_percent_decline_not_greater_than(self, threshold: float = 0.2): """Add condition. diff --git a/tests/base/check_test.py b/tests/base/check_test.py index 101aa05044..9dbe86b06d 100644 --- a/tests/base/check_test.py +++ b/tests/base/check_test.py @@ -8,7 +8,9 @@ class DummyCheck(BaseCheck): - pass + + def run(self): + pass def test_add_condition(): From 8f24aa23e1ec76d7626971ba63f9e1f3a579dd65 Mon Sep 17 00:00:00 2001 From: Matan Perlmutter Date: Tue, 7 Dec 2021 15:21:49 +0200 Subject: [PATCH 3/7] add run wrap --- deepchecks/base/check.py | 9 +- deepchecks/base/display_suite.py | 4 +- deepchecks/base/suite.py | 4 +- .../methodology/datasets_size_comparison.py | 1 - .../class_performance_imbalance.py | 1 - .../examples/CheckSuite_Iris_Dataset.ipynb | 1595 +++++++++-------- 6 files changed, 899 insertions(+), 715 deletions(-) diff --git a/deepchecks/base/check.py b/deepchecks/base/check.py index 017a50a119..77ab4973a8 100644 --- a/deepchecks/base/check.py +++ b/deepchecks/base/check.py @@ -156,8 +156,7 @@ def __init__(self, value, header: str = None, display: Any = None): raise DeepchecksValueError(f'Can\'t display item of type: {type(item)}') def _ipython_display_(self): - header = self.header or self.check.name() - display_html(f'

{header}

', raw=True) + display_html(f'

{self.get_header()}

', raw=True) if hasattr(self.check, '__doc__'): docs = self.check.__doc__ or '' # Take first non-whitespace line. @@ -181,6 +180,12 @@ def __repr__(self): """Return default __repr__ function uses value.""" return self.value.__repr__() + def get_header(self): + if self.header is not None: + return self.header + else: + return self.check.name() + def set_condition_results(self, results: List[ConditionResult]): """Set the conditions results for current check result.""" self.conditions_results = results diff --git a/deepchecks/base/display_suite.py b/deepchecks/base/display_suite.py index f4f2535ac4..f2190163b1 100644 --- a/deepchecks/base/display_suite.py +++ b/deepchecks/base/display_suite.py @@ -67,12 +67,12 @@ def display_suite_result(suite_name: str, results: List[Union[CheckResult, Check for cond_result in result.conditions_results: sort_value = cond_result.get_sort_value() icon = cond_result.get_icon() - conditions_table.append([icon, result.header, cond_result.name, + conditions_table.append([icon, result.get_header(), cond_result.name, cond_result.details, sort_value]) if result.have_display(): display_table.append(result) else: - others_table.append([result.header, 'Nothing found', 2]) + others_table.append([result.get_header(), 'Nothing found', 2]) elif isinstance(result, CheckFailure): msg = result.exception.__class__.__name__ + ': ' + str(result.exception) name = result.check.name() diff --git a/deepchecks/base/suite.py b/deepchecks/base/suite.py index d87678f8bd..187c4318d9 100644 --- a/deepchecks/base/suite.py +++ b/deepchecks/base/suite.py @@ -112,12 +112,12 @@ def run( elif isinstance(check, SingleDatasetBaseCheck): if check_datasets_policy in ['both', 'train'] and train_dataset is not None: check_result = check.run(dataset=train_dataset, model=model) - check_result.header = f'{check_result.header} - Train Dataset' + check_result.header = f'{check_result.get_header()} - Train Dataset' check_result.set_condition_results(check.conditions_decision(check_result)) results.append(check_result) if check_datasets_policy in ['both', 'test'] and test_dataset is not None: check_result = check.run(dataset=test_dataset, model=model) - check_result.header = f'{check_result.header} - Test Dataset' + check_result.header = f'{check_result.get_header()} - Test Dataset' check_result.set_condition_results(check.conditions_decision(check_result)) results.append(check_result) elif isinstance(check, ModelOnlyBaseCheck): diff --git a/deepchecks/checks/methodology/datasets_size_comparison.py b/deepchecks/checks/methodology/datasets_size_comparison.py index 4c10d94cc1..94f1cacf39 100644 --- a/deepchecks/checks/methodology/datasets_size_comparison.py +++ b/deepchecks/checks/methodology/datasets_size_comparison.py @@ -50,7 +50,6 @@ def run(self, train_dataset: Dataset, test_dataset: Dataset, model: object = Non }) return CheckResult( value=result, - header='Datasets size.', display=result ) diff --git a/deepchecks/checks/performance/class_performance_imbalance.py b/deepchecks/checks/performance/class_performance_imbalance.py index 9770688afb..52c0a4d5a5 100644 --- a/deepchecks/checks/performance/class_performance_imbalance.py +++ b/deepchecks/checks/performance/class_performance_imbalance.py @@ -146,7 +146,6 @@ def display(): return CheckResult( value=df.transpose().to_dict(), header='Class Performance Imbalance', - check=type(self), display=display ) diff --git a/notebooks/examples/CheckSuite_Iris_Dataset.ipynb b/notebooks/examples/CheckSuite_Iris_Dataset.ipynb index b5b3728dea..1a72d11daf 100644 --- a/notebooks/examples/CheckSuite_Iris_Dataset.ipynb +++ b/notebooks/examples/CheckSuite_Iris_Dataset.ipynb @@ -205,15 +205,23 @@ "ExecuteTime": { "end_time": "2021-11-23T15:36:31.074802Z", "start_time": "2021-11-23T15:36:25.262617Z" - } + }, + "scrolled": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Overall Suite: 0%| | 0/33 [00:00\n", - "#T_85f9b_ table {\n", + "#T_24e5b_ table {\n", " text-align: left;\n", "}\n", - "#T_85f9b_ thead {\n", + "#T_24e5b_ thead {\n", " text-align: left;\n", "}\n", - "#T_85f9b_ tbody {\n", + "#T_24e5b_ tbody {\n", " text-align: left;\n", "}\n", - "#T_85f9b_ th {\n", + "#T_24e5b_ th {\n", " text-align: left;\n", "}\n", - "#T_85f9b_ td {\n", + "#T_24e5b_ td {\n", " text-align: left;\n", "}\n", "\n", - "\n", + "
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Status
✖
Single Feature Contribution - Train DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8Features with PPS above threshold: petal width (cm), petal length (cm)
✖
Single Feature Contribution - Train DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8Features with PPS above threshold: petal width (cm), petal length (cm)
✖
Single Feature Contribution - Test DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8Features with PPS above threshold: petal length (cm), petal width (cm)
✓
Train Test DriftPSI and Earth Mover's Distance cannot be greater than 0.2 and 0.1 respectively
✖
Single Feature Contribution - Test DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8Features with PPS above threshold: petal length (cm), petal width (cm)
✓
String Mismatch - Train DatasetNo string variants for all columns
✓
Train Test DriftPSI and Earth Mover's Distance cannot be greater than 0.2 and 0.1 respectively
✓
String Mismatch - Test DatasetNo string variants for all columns
✓
String Mismatch - Test DatasetNo string variants for all columns
✓
Data Duplicates - Train DatasetDuplicate data is not greater than 0%
✓
Data Duplicates - Train DatasetDuplicate data is not greater than 0%
✓
Data Duplicates - Test DatasetDuplicate data is not greater than 0%
✓
Data Duplicates - Test DatasetDuplicate data is not greater than 0%
✓
Rare Format Detection - Train DatasetRare formats ratio is not greater than 0
✓
Rare Format Detection - Train DatasetRare formats ratio is not greater than 0
✓
Rare Format Detection - Test DatasetRare formats ratio is not greater than 0
✓
Rare Format Detection - Test DatasetRare formats ratio is not greater than 0
✓
String Length Out Of Bounds - Train DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
String Length Out Of Bounds - Train DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
String Length Out Of Bounds - Test DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
Special Characters - Train DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
Special Characters - Train DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
String Mismatch - Train DatasetNo string variants for all columns
✓
Special Characters - Test DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
Special Characters - Test DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
Label Ambiguity - Train DatasetAmbiguous sample ratio is not greater than 0%
✓
Label Ambiguity - Train DatasetAmbiguous sample ratio is not greater than 0%
✓
Label Ambiguity - Test DatasetAmbiguous sample ratio is not greater than 0%
✓
Label Ambiguity - Test DatasetAmbiguous sample ratio is not greater than 0%
✓
String Mismatch ComparisonNo new variants allowed in test data for all columns
✓
String Mismatch ComparisonNo new variants allowed in test data for all columns
✓
Category Mismatch Train TestNumber of new category values is not greater than 0 for all columns
✓
Category Mismatch Train TestNumber of new category values is not greater than 0 for all columns
✓
Mixed Types - Test DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
String Length Out Of Bounds - Test DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
Mixed Types - Train DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
Mixed Types - Test DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
Mixed Nulls - Train DatasetNot more than 1 different null types for all columns
✓
Mixed Nulls - Test DatasetNot more than 1 different null types for all columns
✓
Dominant Frequency ChangeChange in ratio of dominant value in data not more than 25.00%
✓
Dominant Frequency ChangeChange in ratio of dominant value in data not more than 25.00%
✓
Train Test Samples MixPercentage of test data samples that appear in train data not greater than 10.00%
✓
Train Test Samples MixPercentage of test data samples that appear in train data not greater than 10.00%
✓
Single Feature Contribution Train-TestTrain-Test features' Predictive Power Score (PPS) difference is not greater than 0.2
✓
Single Feature Contribution Train-TestTrain-Test features' Predictive Power Score (PPS) difference is not greater than 0.2
✓
Train-Test Difference OverfitTrain-Test metrics degradation ratio is not greater than 0.1
✓
Train-Test Difference OverfitTrain-Test metrics degradation percentage is not greater than 0.2
✓
Unused FeaturesNumber of high variance unused features is not greater than 5
✓
Unused FeaturesNumber of high variance unused features is not greater than 5
✓
Model Inference Time Check - Train DatasetAverage model inference time for one sample is not greater than 0.001
✓
Model Inference Time Check - Train DatasetAverage model inference time for one sample is not greater than 0.001
✓
Model Inference Time Check - Test DatasetAverage model inference time for one sample is not greater than 0.001
✓
Model Inference Time Check - Test DatasetAverage model inference time for one sample is not greater than 0.001
✓
Datasets Size ComparisonTrain dataset is not smaller than test dataset.
✓
Mixed Types - Train DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
Datasets Size ComparisonTest-Train size ratio is not smaller than 0.01.
✓
Simple Model ComparisonRatio not less than 1.1 between the given model's result and the simple model's result
✓
Simple Model ComparisonRatio not less than 1.1 between the given model's result and the simple model's result
✓
ROC Report - Test DatasetNot less than 0.7 AUC score for all the classes
✓
ROC Report - Train DatasetNot less than 0.7 AUC score for all the classes
✓
Class Performance Imbalance - Train DatasetRelative ratio difference between labels 'F1' score is not greater than 30.00%
✓
ROC Report - Test DatasetNot less than 0.7 AUC score for all the classes
✓
Class Performance Imbalance - Test DatasetRelative ratio difference between labels 'F1' score is not greater than 30.00%
✓
Class Performance Imbalance - Train DatasetRelative ratio difference between labels 'F1' score is not greater than 30.00%
✓
Single Value in Column - Train DatasetDoes not contain only a single value for all columns
✓
Class Performance Imbalance - Test DatasetRelative ratio difference between labels 'F1' score is not greater than 30.00%
✓
Single Value in Column - Test DatasetDoes not contain only a single value for all columns
✓
Single Value in Column - Train DatasetDoes not contain only a single value for all columns
✓
Mixed Nulls - Train DatasetNot more than 1 different null types for all columns
✓
Single Value in Column - Test DatasetDoes not contain only a single value for all columns
✓
ROC Report - Train DatasetNot less than 0.7 AUC score for all the classes
✓
Mixed Nulls - Test DatasetNot more than 1 different null types for all columns
✓
New Label Train TestNumber of new label values is not greater than 0
✓
New Label Train TestNumber of new label values is not greater than 0
\n" @@ -530,7 +550,7 @@ }, { "data": { - "image/png": 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xWwIaRORY3BaWtmwB4rjjZrrC94AvqOrOxIVJHrfHcQehX8Lubitwj9+VXmuPiEi+uIOuC1sKQEQOEZEveGNWGnATzXh7gYs7KPoYYDKwHfhnC9uM8+II4I6HakgoexN7Hsd83ORni/fa7+K26LTL6w57FbhPRPqKSEBEmsZTdeh4GJNulugY43pRRGpwf63+FveaON9t+yWtU9XFuONTVnrN+y016+fhdo9U4Q4eHop7xg2quga3ZeJ/cLsw5gCjvdf9DPdLbiVui9PjuINDW/MrYDkwTUSqgbeAQzpbN0/TWVJNt2u85T8GbvKO5e9xk4tWed0lt+KeTl0lIns1zkNVV6hqWSur2zxuqvqJt35f3C/5puVlwA9wu4y24x7Ly9oII4Q7AHwrbtfQPsCv29j+l97xqsTtcpsJHN88WfMU4SYa23G73iqB2711D+OOC6oSkcmquhB3HNLHuEnQEcCHbcTR3LdxW8EWA5txu+M6czyMSStR7bKWY2OMMcaYHsVadIwxxhiTtSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZK1UTfiXMqWlpTps2LB0h2GMMcaYFJo5c+ZWVW3x4qYdkXGJTmVJH7bd+qt0hwFA/IyvpTsEY4wxJiuJyOr2t2qfdV0ZY4wxJmtZomOMMcaYrJURiY6IXCEiZSJSxo7qdIdjjDHGmAyREYmOqj6gqmNVdSx9Wp2o1xhjjDFmDxmR6BhjjDHGdIYlOsYYY4zJWpboGGOMMSZrZUSiY4ORjTHGGNMZGZHo2GBkY4wxxnRGRiQ6xhhjjDGdYYmOMcYYY7KWJTrGGGOMyVqW6BhjjDEma2VEomNnXRljjDGmMzIi0bGzrowxxhjTGRmR6BhjjDHGdIY/3QF01DFFfSk742vpDsMYY4wxGUBUNd0xdIiI1ABL0h1HGpUCW9MdRJr05rpD765/b6479O769+a6Q++u/yGqWri3hWRciw6wRFXHpjuIdBGRst5a/95cd+jd9e/NdYfeXf/eXHfo3fUXkbKuKMfG6BhjjDEma1miY4wxxpislYmJzgPpDiDNenP9e3PdoXfXvzfXHXp3/Xtz3aF3179L6p5xg5GNMcYYY5KViS06xhhjjDFJsUTHGGOMMVmrRyU6IvIlEVkiIstF5PoW1odE5Clv/SciMixh3a+95UtE5IxuDbwLdLbuIjJMROpFZI53u7/bg+8CSdT/8yIyS0SiInJhs3WXisgy73Zp90XdNfay7rGE9/6F7ou66yRR/2tEZKGIfCoib4vI0IR12f7et1X33vDeXyki87w6fiAihyesy/bP/Bbr3ls+8xO2+6qIqIiMTVjWsfdeVXvEDfABK4ARQBCYCxzebJsfA/d7jy8GnvIeH+5tHwKGe+X40l2nbqr7MGB+uuvQDfUfBhwJPAJcmLC8BFjp3ff1HvdNd526o+7eutp016Eb6n8KkOc9/lHC335veO9brHsveu+LEh6fC7zmPe4Nn/mt1b1XfOZ72xUC7wPTgLGdfe97UovOscByVV2pqmHgSeC8ZtucB/zLe/wMcKqIiLf8SVVtVNVVwHKvvEyxN3XPBu3WX1XLVfVTIN7stWcAb6rqNlXdDrwJfKk7gu4ie1P3bJBM/d9V1Trv6TRgiPe4N7z3rdU9GyRT/+qEp/lA09kzWf+Z30bds0Ey33kANwN/ABoSlnX4ve9Jic5gYG3C8wpvWYvbqGoU2AH0S/K1Pdne1B1guIjMFpH3ROTEVAebAnvz/vWG974tOSJSJiLTROT8Lo2se3S0/t8DXu3ka3uavak79JL3XkR+IiIrgD8CV3XktT3Y3tQdesFnvogcDeynqi939LXNZeIUEGZPG4D9VbVSRI4BJovIyGa/Bkz2Gqqq60RkBPCOiMxT1RXpDioVRORbwFjgpHTH0t1aqXuveO9V9V7gXhH5JvA7IOPGYnVWK3XP+s98EXGAO4DLuqK8ntSisw7YL+H5EG9Zi9uIiB/oA1Qm+dqerNN195rvKgFUdSZuf+XBKY+4a+3N+9cb3vtWqeo6734lMAU4qiuD6wZJ1V9ETgN+C5yrqo0deW0Ptjd17zXvfYIngfM7+dqeptN17yWf+YXAKGCKiJQDnwNe8AYkd/y9T/egpIRBR37cwYTD2T04aWSzbX7CngNy/+M9Hsmeg5NWklkD0/am7v2b6oo7sGsdUJLuOnV1/RO2ncRnByOvwh2M2td7nDH138u69wVC3uNSYBktDOjrybck//aPwv0wP6jZ8qx/79uoe2957w9KeHwOUOY97g2f+a3VvVd95nvbT2H3YOQOv/dpr3CzynwZWOr9Y//WW3YT7i8ZgBzgadzBR9OBEQmv/a33uiXAmemuS3fVHfgqsACYA8wCzkl3XVJU/3G4fbE7cVvxFiS89nLvuCwHvpvuunRX3YHjgXneP/084HvprkuK6v8WsMn7G58DvNCL3vsW696L3vu/Jny+vUvCl2Ev+Mxvse695TO/2bZT8BKdzrz3NgWEMcYYY7JWTxqjY4wxxhjTpSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtSzRMcZ0mojcLyI3dFFZ+4tIrYj4vOdTROT7XVG2V96rInJpV5VnjMkM/nQHYIzpuUSkHBgARIEYsBB4BHhAVeOqemUHyvm+qr7V2jaqugYo2NuYvf1NBA5U1W8llH9mV5RtjMks1qJjjGnPOapaCAwFbgN+BTzclTsQEfvRZYxJCUt0jDFJUdUdqvoCcBFwqYiMEpFJInILgIiUishLIlIlIttEZKqIOCLyKLA/8KLXNfVLERkmIioi3xORNcA7CcsSk54DRGS6iFSLyPMiUuLt62QRqUiMT0TKReQ0EfkS8BvgIm9/c731u7rCvLh+JyKrRWSziDwiIn28dU1xXCoia0Rkq4j8NrVH1xiTKpboGGM6RFWnAxXAic1W/Y+3vD9ud9dv3M3128Aa3JahAlX9Y8JrTgIOA85oZXffAS4HBuF2n92VRHyvAf8PeMrb3+gWNrvMu50CjMDtMrun2TYnAIcApwK/F5HD2tu3MabnsUTHGNMZ64GSZssiuAnJUFWNqOpUVdV2ypmoqjtVtb6V9Y+q6nxV3QncAHy9abDyXroEuENVV6pqLfBr4OJmrUn/q6r1qjoXmAu0lDAZY3o4S3SMMZ0xGNjWbNntwHLgDRFZKSLXJ1HO2g6sXw0EgNKko2zdvl55iWX7cVuimmxMeFxHFw2UNsZ0L0t0jDEdIiLjcBOdDxKXq2qNqv6Pqo4AzgWuEZFTm1a3Ulx7LT77JTzeH7fVaCuwE8hLiMmH22WWbLnrcQdXJ5YdBTa18zpjTIaxRMcYkxQRKRKRs4EngcdUdV6z9WeLyIEiIsAO3NPR497qTbhjYTrqWyJyuIjkATcBz6hqDFgK5IjIWSISAH4HhBJetwkYJiKtfcY9AfxCRIaLSAG7x/REOxGjMaYHs0THGNOeF0WkBrcb6bfAHcB3W9juIOAtoBb4GLhPVd/11v0f8DvvjKxrO7DvR4FJuN1IOcBV4J4BBvwYeAhYh9vCk3gW1tPefaWIzGqh3H94Zb8PrAIagJ91IC5jTIaQ9scKGmOMMcZkJmvRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtTJuIr3S0lIdNmxYusMwxhhjTArNnDlzq6r2b3/LtmVcojNs2DDKysrSHYYxxhhjUkhEVre/Vfus68oYY4wxWcsSHWOMMcZkLUt0jDHGGJO1Mm6MjjHGGLM3IpEIFRUVNDQ0pDsUA+Tk5DBkyBACgUBKyrdExxhjTK9SUVFBYWEhw4YNw52D1qSLqlJZWUlFRQXDhw9PyT6s68oYY0yv0tDQQL9+/SzJ6QFEhH79+qW0dc0SHWOMMb2OJTk9R6rfC0t0jDHGGJO1bIyOMcaYXu2CCyZTXl7dZeUNG1bEc8+d3+r6yspKTj31VAA2btyIz+ejf3/3AsDTp08nGAy2+tqysjIeeeQR7rrrrk7FNmnSJMrKyrjnnnta3WbKlCkEg0GOP/74Tu2jJVVVVTz++OP8+Mc/7rIyk2WJjjHGmF6tvLyawYMLurS8tvTr1485c+YAMHHiRAoKCrj22mt3rY9Go/j9LX89jx07lrFjx3ZZrC2ZMmUKBQUFXZ7o3HfffWlJdKzrqhtE4zGWbNnA2ysXsr56O6qa7pCMMcb0IJdddhlXXnkl48eP55e//CXTp0/nuOOO46ijjuL4449nyZIlgJuEnH322YCbJF1++eWcfPLJjBgxotVWnn/+858cfPDBHHvssXz44Ye7lr/44ouMHz+eo446itNOO41NmzZRXl7O/fffz5133smYMWOYOnVqi9sBvPfee4wZM4YxY8Zw1FFHUVNTA8Dtt9/OuHHjOPLII7nxxhsBuP7661mxYgVjxozhuuuuS9lxbIm16KTQjoZ6Pt20lpnry2mIRlGUGevKKc0r4HNDRnBw6UCCPnsLjEm1xmiEpVs3snjrRnIDAYpCeRSGcsgLBMgNBMkLBMnzB8kJBHFskKpJk4qKCj766CN8Ph/V1dVMnToVv9/PW2+9xW9+8xueffbZz7xm8eLFvPvuu9TU1HDIIYfwox/9aI/r0WzYsIEbb7yRmTNn0qdPH0455RSOOuooAE444QSmTZuGiPDQQw/xxz/+kT//+c9ceeWVe7Qybd++vcXt/vSnP3HvvfcyYcIEamtrycnJ4Y033mDZsmVMnz4dVeXcc8/l/fff57bbbmP+/Pm7WrK6U0q/ZUXkS8BfAR/wkKre1sI2XwcmAgrMVdVvpjKmVIursmZHJTPWrWLFts0A5PqDFAZC1NZGyM3zs6OhnpeXzuX15fM5atBQxgzan5Lc/DRHbkx2UVU276xhzobVfLq5glg8jk8cFIhrHBQcRxAE9bZXlBx/gPxgiIJgDoMK+jB28DAKgjnpro7pBb72ta/h8/kA2LFjB5deeinLli1DRIhEIi2+5qyzziIUChEKhdhnn33YtGkTQ4YM2bX+k08+4eSTT941Buiiiy5i6dKlgJtYXXTRRWzYsIFwONzqdWxa227ChAlcc801XHLJJVxwwQUMGTKEN954gzfeeGNXMlVbW8uyZcvYf//9u+YgdULKEh0R8QH3AqcDFcAMEXlBVRcmbHMQ8GtggqpuF5F9UhVPqtVHwizYvJ5PKlZSG25ABAqCOTgibNvWwIrl22kMxwEoLAzQr18uhX0cplesZPq6VQztU8KxQ0YwrLgUn2M9isZ0VjgWZcnWjcxYt4rNO92xEvmBEL5A+/9XqkpMlZ3hRqob6lldtZUZ61YxbvBwjh0ygrxA64NEjdlb+fm7f/DecMMNnHLKKfz3v/+lvLyck08+ucXXhEKhXY99Ph/RaDTp/f3sZz/jmmuu4dxzz2XKlClMnDixQ9tdf/31nHXWWbzyyitMmDCB119/HVXl17/+NT/84Q/3KKO8vDzpuLpaKlt0jgWWq+pKABF5EjgPWJiwzQ+Ae1V1O4Cqbk5hPF1OVdlYW83M9eUs3LKeuMYJ+QIUhtxff3V1UVasqKKqqhG/TwiFfKgqO3dGqa7egYjgOEKf4iA7+jWyfMsW+uTnMn7IcEbtM4T8YKidCIwxTTbXVjNn4xo+3VRBLB7D7/goDOZ06BodIoJfBHDAB7lALB5nWsUKytaXM37ICMYNHk6OPzWXqjemyY4dOxg8eDDgninVWePHj+fqq6+msrKSoqIinn76aUaPHv2ZffzrX//a9ZrCwkKqq3cPqG5tuxUrVnDEEUdwxBFHMGPGDBYvXswZZ5zBDTfcwCWXXEJBQQHr1q0jEAhQWFi4awxPd0tlojMYWJvwvAIY32ybgwFE5EPc7q2Jqvpa84JE5ArgCiCtzV+J6iNh/rNgBhtqqhCEvEBwV0tMNBJn9ZpqNqzfiQjkhBzwPmxFhEBACHi/LuNxpWp7I9srG0EgmLuDFWu2UVgwj+OHH8CZh46yC1sZ04pwLMrSrZuYsX4lm2qrEYTcQBB/F7a8+ByHolAu0XiMD9csY3rFSo7f70CO3ncoIUt4ssKwYUVdfnr53vrlL3/JpZdeyi233MJZZ53V6XIGDRrExIkTOe644yguLmbMmDG71k2cOJGvfe1r9O3bly984QusWrUKgHPOOYcLL7yQ559/nrvvvrvV7f7yl7/w7rvv4jgOI0eO5MwzzyQUCrFo0SKOO+44AAoKCnjsscc44IADmDBhAqNGjeLMM8/k9ttv7/zB6SBJ1RlAInIh8CVV/b73/NvAeFX9acI2LwER4OvAEOB94AhVrWqt3LFjx2pZWVlKYk5WNB7jiXmfsL66ioJgaFcioqps2LCT8vJq4jElGHQQpwNJiiqxOESjcRDI7SOce/QRnDdmdIpqYkzmqtixjWcXzqQ+Gibg+MjxB7rlR0E0HqMuEibo83PC0IMYM3B/O6kgwyxatIjDDjss3WGYBC29JyIyU1X3+lz6VP53rgP2S3g+xFuWqAL4RFUjwCoRWQocBMxIYVx7RVV5bdk8Kqq3U5TQLL59ewMrVlRRXx8jEHB2tdh0iAg+H7sGozVUx3hh1jwKAiFOHXloV1bDmIy2YPM6Xloyl4DPR1Eot1v37XfcfUZiMd5ZuYiP1iznxKEHc+SA/Qh4/7vGmJ4jlaNeZwAHichwEQkCFwMvNNtmMnAygIiU4nZlrUxhTHtt2toVzNtUsavvv74+yvz5W5k/v5JwOE5Ojg+fr2t+VQYDPhprlMc/KePjpau6pExjMpmq8n75El5YPIccfyCtY2USk6w3VyzgbzPeYeGWdXadLGN6mJS16KhqVER+CryOO/7mH6q6QERuAspU9QVv3RdFZCEQA65T1cpUxbS3Fm3ZwJTyJbvOplq1agfr1tUCe47D6UoBv4/G2hgPTf2IXH+AMSOGtP8iY7JQJBbjlaVzWbhlPQXBnB5zdmLQ5yfo8xOORXl+0WzWDKrk1BEjrXXHmB6i3URHRP6Ml6R0tHBVfQV4pdmy3yc8VuAa79ajra/ezotL5pAbCOBzHNauraGiooZQ0NexcTidEPD5CNfHuPvt97jWfxqH7T8gpfszpqepDTfy7IIy1tdWURTK7ZED9IM+P37Hx+wNa1lfU8UFh42lODcv3WEZ0+sl85NoEfCAiHwiIleKSJ9UB9XT7Gio4z8LZuATIejzs6OqkdXl1QS7Iclp4hcfkUic2195i5Xre2yjlzFdbuvOGibN/oCNO3fsMS6uJ3JEKArlsLVuJw/Pep9llZvSHZIxvV67iY6qPqSqE4DvAMOAT0XkcRE5JdXB9QQN0QhPzZ9OJBYjNxAkHI6xcFElPr97DZzu5FMfUY1zy39fp2LTjm7dtzHpsHLbFibN+ZD6SLjD18RJFxGhIBjCEYdnF5bx7qrFxOLxdIdlTK+V1Bgd7yrHh3q3rcBc4BoR+aGqXpzC+NIqFo8zedEsttXXURTKQVVZtLCSWEwJhdLT/+6L+4j6Y9zwxCv83yVnM7B/YVriMCaVVJXZG9bwxor5hHwBQoHMO3075PfjdxymrV3BuurtnH/YUTaVRA/19xlT2FZf22XlleQW8MNxJ7e6vrKyklNPPRWAjRs34vP5dk3RMH36dILBtq8DNWXKFILBYIuzi0+aNInrrruOIUOGUFtby4gRI7jxxhvbnYl88uTJHHzwwRx++OHt1C555eXlfPTRR3zzm+md2andFh0RuRNYDHwZ+H+qeoyq/kFVzwGOSnWA6aKqvLVyIau2b6XQu0LxqlXVVNeECQXTOwjSF3WI50W5/p+vsLWyLq2xGNPVYvE4b61cyGvL55EXCBLyZ16S08S92GAO62q28/DMqaypsm7nnmhbfS19cvK67NZe0tSvXz/mzJnDnDlzuPLKK/nFL36x63l7SQ64ic5HH33U6vqLLrqI2bNns2zZMq6//nouuOACFi1a1GaZkydPZuHChW1u01Hl5eU8/vjjXVpmZyTzjf0pMEZVf6iq05utOzYFMfUIZevLmbl+NYUht7m8srKedetq3JactDefC07EQYvDXHv/S1RVNaQ5HmO6RkM0wrMLyyhbV05RKBe/k/lnLokIhcEcYhrn8XnTmLZ2BXE7Bd00M3PmTE466SSOOeYYzjjjDDZs2ADAXXfdxeGHH86RRx7JxRdfTHl5Offffz933nknY8aMYerUqW2We8opp3DFFVfwwAMPAPDggw8ybtw4Ro8ezVe/+lXq6ur46KOPeOGFF7juuusYM2YMK1asaHE7gKeffppRo0YxevRoPv/5zwMQi8W47rrrGDduHEceeSR///vfAXcurKlTpzJmzBjuvPPOVB26diWT6HxLVXcmLhCRtwFUNSsHiiyv3MTbKxdSEAzieNfKWbJkG4GA04PGCHjJTv9Grr7zJXbssGTHZLbGaIQnPp3Gyu1bKAq5l3DIJjn+AHmBEO+uWsyzC8qoj4TTHZLpIVSVn/3sZzzzzDPMnDmTyy+/nN/+9rcA3HbbbcyePZtPP/2U+++/n2HDhu3RCnTiiSe2W/7RRx/N4sWLAbjggguYMWMGc+fO5bDDDuPhhx/m+OOP59xzz+X2229nzpw5HHDAAS1uB3DTTTfx+uuvM3fuXF54wb003sMPP0yfPn2YMWMGM2bM4MEHH2TVqlXcdtttnHjiicyZM4df/OIXKTp67Wu1TVhEcoA8oFRE+gJNnzpFuPNYZaVNtdX8d/EscvwB/I6PeFxZuLASVfD5esZ1O3YTnIgQH1LPVbe9zD2/OYfCQptd2WSeWDzO5MWz2bSzOmMGHXeG3+vKWrF9M/+YNZULR45lQEGvO5HVNNPY2Mj8+fM5/fTTAbeFZNCgQQAceeSRXHLJJZx//vmcf/75nSo/8SKW8+fP53e/+x1VVVXU1tZyxhlntPia1rabMGECl112GV//+te54IILAHjjjTf49NNPeeaZZwB3EtBly5Yl1Q3XHdr65v4hMBN3APIs7/FM4HngntSH1v1qGut5av50BNk1d82yZdupq4sSDPbUJnQHJyLo8Dp+86c37aqsJuM0jYdbuW1zVic5TUSEolAu9dEIj8z5iEVb1qc7JJNmqsrIkSN3jdOZN28eb7zxBgAvv/wyP/nJT5g1axbjxo0jGo12uPzZs2fvmkfqsssu45577mHevHnceOONNDS03BvQ2nb3338/t9xyC2vXruWYY46hsrISVeXuu+/eFf+qVav44he/2Mmj0fVaTXRU9a+qOhy4VlWHJ9xGq2pWJjovL/2U+kiYPG/m440bd7J5cx2hUE9ryWlGHfxxYXv/Sia/vCTd0RjTIdPXrWLW+tUU9tALAaZKXiBIwOdj8qLZTFm1mLjaKei9VSgUYsuWLXz88ccARCIRFixYQDweZ+3atZxyyin84Q9/YMeOHdTW1lJYWEhNTU1SZb/33ns88MAD/OAHPwCgpqaGQYMGEYlE+Pe//71ru+ZltrbdihUrGD9+PDfddBP9+/dn7dq1nHHGGfztb38jEokAsHTpUnbu3NmhOFOpra6rL6jqO8A6Ebmg+XpVfS6lkaXBjsb6XWd41NZGWL68ikDAlxEfvhpzCOTAv6eVcfy4IQwYUJDukIxp1+ItG3h31SLyg6GsG5OTjKDPj08cPl67gk07qznv0KPSOn9Xb1WSW9Dlp5d3hOM4PPPMM1x11VXs2LGDaDTKz3/+cw4++GC+9a1vsWPHDlSVq666iuLiYs455xwuvPBCnn/+ee6+++7PjNN56qmn+OCDD6irq2P48OE8++yzu1p0br75ZsaPH0///v0ZP378rkTk4osv5gc/+AF33XUXzzzzTKvbXXfddSxbtgxV5dRTT2X06NEceeSRlJeXc/TRR6Oq9O/fn8mTJ3PkkUfi8/kYPXo0l112WdrG6UhrXR0i8r+qeqOI/LOF1aqql6c2tJaNHTtWy8rKUlL238umUB8J46jDrFmbiEbjBAI9tcuqJUo8EKdoXQl3TfxyRiRopvdaX72dxz6dRtDn29VV3FupKrXhRvrk5PK1kePol2c/VFJp0aJFu774Tc/Q0nsiIjNVdezelt3qp4uq3ujdf3dvd5JplizZTrgxTignk5IcAMEXF7b1q+T5l5dy/tmHpDsgY1q0rX4nT82fjk+cbklyVKGxMUokHMfndwgEHPx+J/1XivCICIWhHGrCDUya/QHnH3Y0B5Tsk+6wjMkKyUzqeTXwT6AGeBA4GrheVd9IcWxpsWH9TrZtqycnTVc+3lsacwjmwaMfzOD4Y4ewzz756Q7JmD3URcI8NW860XicfO9inF0lEonTUB+loSFKXX2U2toIdXURGhtj7mmjXmKjincmpRAIOASDPoJB9z4n5CMQ9OH3O4RCPvLy/N3WOpofCNEYjfL0ghmcMuxQjh0ywlpmjdlLyfyUulxV/yoiZwD9gG8DjwJZl+hUbW+koqLWPcMqkz9cGoWCEXFuvvs97rrpTPugND1GJBbj2QVlVDfWUxjau+kQwo0xtmytp6YmTF1dhPr6KKruv+7uRMYd/xAMtnANLFVUIRZT6nZGqK1V73WKIIgDeD37hUVB+vbNoagoSGFhMKXz3DVNHfHOqsVs2lnNmQcdScCXmT+8ejJVtc/GHiLVZwsnk+g0/SV8GXhEVRdIlv51LFu+HcdHFgyKFHwqVJZs5YWXl3He2QenOyBjiKvyyrK5VFRvp2gvkpyqqkbWratl+/YGVBXHcSfY7fAFPUUQAR+Ar/XXqSq1tRF27AjjiJv75OcH6Ns3RJ8+IQoLg/j9XXtmZtPUEQs3r2drXQ1fPXwcfXJyu3QfvVlOTg6VlZX069fPkp00U1UqKyvJyUndPHDJJDozReQNYDjwaxEpBLLyPMh4THESfsVlMo06hAph0pTpHHfsYOvCMmk3tXwJCzdvoCjU8WvlRCJxNm+uY11FDeFIHBFabqVJAREhEBAC3slQqkpDQ5S1ayNUVNSCQk6uj+LinF3JT1ckPk3jdrbW1fLP2e7FBYcUlex1uQaGDBlCRUUFW7ZsSXcoBjfxHDJkSMrKb/Wsq10biDjAGGClqlaJSD9gsKp+mrKo2pDKs64uv+cJYk4UR7OkmViUuD9On9X9+estZ9gvF5M2czas4dVl8ygIhvA5yScBNdVh1m+oZcuWelDw+wVfF7ee7DVVYjElGvM+SxUKCgKU9s+lb98c8vP3/nTxhmiESCzGKcMPZezgYTjSw46BMSmQ8rOumqhqXEQ2AYeLSO8+BzTTqOBD2FKymRdfXs65Zx+U7ohML7Ry2xZeXz6f/GAwqSQnFlO2bKmjoqKWhvooCAQDDpLCcTF7RQSfX2g6eUxVqa+PsmrVDsrLq/H5hJKSHEpLczvd2uNOSePw9qpFLNm6kXMOGUNxbl4XV8SY7JTMWVd/AC4CFgIxb7EC76cwLtNFNOKQ2wf+8dYnfO7Yfa0Ly3SrzbXVPLdoJkGfr92ZyGMxZfXqajZsqEXj4POLe1XyDGuJFBH8AcEfcBOaeFzZsqXebZXCHd/Tv38uxcU5FOQHdo+CbIff8VEUzGFDbRUPznyP0w8YyeiB+1lLrTHtSKaF5nzgEFVtTHEsJkU0LPQ5PM4tf5nKX2+1LizTPeoiYZ5eMANQQv62J/fbtq2BpUu3E4nECQadlJ7V1N0cRwh5l6tQVRrqo6xaVY1INY4DRUUhSkpyKCoMkp8faLPlSkQoCOYQicV4ddk8llRu5MsHHbnXZ7AZk82SSXRWAgHAEp1MpYJfhM3Fm3np5eWcY11YJsXiGueFxbOpDTe2+SUcicRZsaKKLVvq8PsdcjLuIp0ds7u1x30ejyvV1Y1s395IU35TUBh0E582TmUP+HwUOTmUV23lwZnvceZBR3Bo6SD7EWNMC5JJdOqAOSLyNgnJjqpelbKoTJeLhx1y+8GDr33CeOvCMin2weplrNq+tc3TyLdsrmP58ipiMSUUyow55bqae2q8b48zunbujFBdveep7G7iEyI/z0/Qax0SEQqDOYRjUSYvms1h/TdyxoGjyA203XpmTG+TTKLzgnczma5R6HtEnFvu/IC//r8v9sovFpN6yys38eHa5RQEQy3+jTU2xli2bDvbtzcQCDiEMmo+udRq7VT2NWtqEHEnVRQgN9dPfkGAgoIgubl+cnL8LNqygdU7Kjn74NE2fYQxCZI56+pfIpIL7K+qS7ohJpMqKvh9wsbCTbz08grOOfvAdEdkssy2+p08v3g2Ob7AZ8+wUli/oZZVq3agijvNiiXbbRIR/H7Bn/BJrao0hmPUb42yeXPdrgucqkJOgbBk5TsM0BLGlR7AgNJ8SkpyKCnJpagoaD9uTK+UzFlX5wB/AoLAcBEZA9ykquemODaTAvFGIX+AwwMvTmP8sYOsC8t0mXAsyrMLyoipkhvY86Olri7K0qXbqKkJEwj48LVxJWLTtqbkpzlVJdoI1Q0RakMbWbJ5E1tnQHW5EIsoIlBc7J7mPmBAPoMG5bPvvgWUlrrX+ykpyaG4OERxcYiAtbKZLJJM19VE4FhgCoCqzhGRESmMyaSUQBj6HhXn9numcftNp6Y7IJMFVJXXls2jsr6WotDuqQo0rqytqGXNmmpErBUnlUQEnw98+CAG/nxlv9OUeKOwc2GAnat9RBqUDRvc9yMcjhONxvH7d5/lFo8rsVicvDx3XFC/frnss08egwblM2BAHn375lBc7CZETeuz6Qw5k52SSXQiqrqjWZNnVk4B0WvEBX+Ow0pnHZ98sp7x4/dNd0Qmw83asJoFm9fvMfg4HI6xYEEltbVhgkGffSF2M40IGhHErxQeFaVgVJSdS/zUlwfQSNtze8Vi7qDoqqpGFi+uJBKJE4/rHklRLKaA0r9/HkOGFDJsWBHDh/dhwAA3KRowwO02s+4yk27JJDoLROSbgE9EDgKuAj5KbVgm1bQBioYIf570IY8ddYE7Y7sxnVBRvZ23ViwkP7h7DEhNTZgFCyqJRePWipNmGhU0CviUglERCg6LsnO5n/oVfuKNn31fdo8LcshtZx7ReFwJh2MsWlTJnDmb92ghisXigDBggJsIHXhgMYccUsL++xcxdGgRffqEUlJfY5pLJtH5GfBb3FPLnwBeB25OZVCmOwgScwgc1sDjTy3gsm8fme6ATAaqDTfw7MIy/M7uKx9v3lzH0qXbcRzZdSq06QFiQrxewFHyD4mQf3CUulU+6pb5idd1bu4sxxFyctyzvlrcZcydDmP+/K3MnLkRVfD5HKLROIWFQQ44oA8jR5Z6CVAh++9fRFGRJUCmayVz1lUdbqLz29SHY7qTRoVQnvDfWXP50ukHMHCgDUw2yYvF4/x30SwaohEKgzmoQvmqHVSsq7EBxz1Z3Et4RMkbESVvRIyGNT52LvMTqxaSnpMiCT6fkJvrJzfXD+xOYFSVSCTO0qXbmTt3CyD4fEI0GqdPnxAHHljMEUeUcuih/Rgxog/77VdoA6RNp7WZ6IjIpcDVwCHeokXAXar6SKoDM91Dw0KfQ5Xb7/uYP990WrrDMRlkSvliKqq3UxTMIRZVFi2uZPv2BkJBX8+dgNPsprsTnpz9YuTsHyVe79CwziG80Ud4mwOx1LyPIkIw6CMY9O3RhdWUAC1aVMnMmZu8CyoK8bgydGgRo0aVcsQRpYwYUczw4X2s+8skpdVEx0tyfg5cA8zCTfOPBm4XEVXVR9srXES+BPwV8AEPqeptrWz3VeAZYJyqlnW0EmYveNfWWRvawLRp6/jc5wanOyKTARZtWc/0ilUUhkI0NMRYsGArDfUxG4+TiVSIN7gPxafkHRgj74AoIIS3ODRUOES2+ojt7NrWnpa0lgDF40plZT2vvrqSl15agd/vdn8VF+dw6KEljBnTn4MO6svw4X3Yd98CfL7OdcWZ7NRWi86PgK+oannCsne8pORJoM1ER0R8wL3A6UAFMENEXlDVhc22K8RtNfqk4+GbrqCNQv4A5Y5/f8hjR3/VBiabNm3dWcNLS+eSGwhQXRVh4aJKVJVQls9Tlf0EjYHWu48RJdgvTnCfGBAlXi/d0trTEscR8vMD5OcHdi1TVcLhOLNnb+Kjj9YRCDiouusOOKCYkSP7MWpUKcOHu60/hYU2NUZv1VaiU9QsyQFAVctFpCiJso8FlqvqSgAReRI4D1jYbLubgT8A1yUVsUkBQaIOwUMaeeSJeXz/0jHpDsj0UA3RCE8vKAOFLRsbWLlyB36/2PiJbKRCPAxuK45+prUnUunQuNkhWuUQ3eF4rULdl/yIuLPCh0I++vbdvTwWi7NhQy0rVlQxefLyXYOfS0rc1p/Ro3e3/gweXGiXPegF2kp06ju5rslgYG3C8wpgfOIGInI0sJ+qviwirSY6InIFcAXA/vvvn8SuTUdpTAjmCC/Nm8dZGw5k0KCCdIdkepi4Ki8tmUtVfR2b1jawadNOuz5Or/HZ1h5/3zjB/jE0LiCgEQhXOkS2uIlPZIeDhrv/b8PncygoCFJQsLsFx239iTFr1iY+/HAdfr/bteU4wkEH9eXII/szcmQ/RowoZtiwIkKhZE5INpmirXfzMBH5tIXlAuz1lZFFxAHuAC5rb1tVfQB4AGDs2LG6t/s2LdNGoc/Byh//9hF3/O/pdqEvs4eP1ixj0eYNVCyrpbom2mtnHDeAChqG2K5ERsGBUP84oUEx95KyIsQbhUilO9YnWuUjUiUQ7/6/Gbf1x08o5P9M68+aNdUsXlwJgOM4xGJxhgwpZNSoUkaP7s8BBxQzYkSxDXzOYG0mOntZ9jpgv4TnQ7xlTQqBUcAU78NyIPCCiJxrA5LTRfCJsD5/Ix9PW8fxxw1Jd0Cmh1hWuYl3li9m1ZIaGhvi5IQcG3RsEgjEcbu6EpMfnxIaqIT2jeFetRDCWx0aN7gDnKPVApq+vyOfz6GwMLjH+J14XKmubuStt1bz6qur8Pvd5Gfw4AJOOGEIxx47kFGjSikpaedqiqbHaDXRUdXVe1n2DOAgERmOm+BcDHwzofwdQGnTcxGZAlxrSU56aVjI7Sf85akPOebor1oTrqGyrpan5sxgxZJqoo1KyC4CaJIiEIN4zHsMIEqgxO3yQqNoHMKbvcSn0kesNvVndrXHcYS8vAB5eXsOfK6uDvPUU4t5+uklxOPKoEH5TJgwmGOPHcQRR5RSWpqXxqhNW1L2LaaqURH5Ke6VlH3AP1R1gYjcBJSp6gup2rfZG4JEHEIHh/nXE/O44rKj0h2QSaOGaISHP/6A5cu2EwtjZ+SZveN1eWlC4hMa4HV3aQSNCo0bHcKbfIQ3+1qcoiIdRPY860vVnQvs2WeX8txzy4jHlQED8pkwYV/Gj9+XI4+0xKcnEdXMGvIyduxYLStLTaPP5fc8QcyJ4qh9mEtOnKoVyr0/+4oNTO6l4hrnnrffY+bKCuJ1gj9g1yYxKeYoTsJZ4A3rHepX+IlUOqS7pactqu5UFzU1kV3Pjz56AF//+iFMmDC41SkyTNtEZKaqjt3bcto9+iJyDvCyqtqM5b2INgiFw5Tb7v+Av0w8wwad9kIPvf4JM9dUEK/HkhzTPeK7L16IKDn7xskZHCZWJ9Qt9dFQ4W9z5vV0EdmzuyseV+bP38rs2ZsIBn2cffYBnHvuARx2WD/7LE2DZD69LgKWicgfReTQVAdkegrBQdhctIUPP65IdzCmm/1jchkfrlmONgp+v7VwmjRQId4gxOvBCSiFY6L0P6uBoqPD+IvjQM/tjXAcoaQkhwED8snPD/Dss0u59NJXOf/8yTzxxCK2bq1Ld4i9SruJjqp+CzgKWAFMEpGPReQK74rGJptFHEJFcNezH9LYGE13NKYbqCp/f3Qm765fiMQEn2MtOSbdBI2683LFGyFn/xglpzRScmojOftFEX/PTXjAHdc2cGA+AwbkUV3dyJ//XMaXv/wsP/3pW7z//lrC4Vi6Q8x6SX2KqWo17lxUTwKDgK8As0TkZymMzfQAEnXIOTDCP5+Ym+5QTIqpKn+5p4wpmxfg9ztI3JIc08MktPL48pSisRH6f7mBgtFhnLyePbpCRCgoCDJoUD79++cxa9Ymrr12Cqed9h/uumsWO3Y0pjvErNXuJ5mInCci/wWmAAHgWFU9ExgN/E9qwzNpFxf8AeGtlQupqKhOdzQmRaLROLfc+jFTNy0ir9iBiCU5picTNOK18kQhb0SU0i82kn94BAn07BYecLu2+vXLZcCAfHJz/Tz66ALOOec5nnxykbXwpEAyn2YXAHeq6hGqeruqbgZQ1TrgeymNzvQI2ijkD4Gb73uPTDtLz7QvHI7xm99M5aOK5fQd4RBvsMGSJoPEhXi9Q7wR8g+JUHpGAzlDIyCZ8VnV1LUVDPr405/KuOCCyUyZssY+a7tQMonORlV9P3GBiPwBQFXfTklUpocRfHFh56DtPPvi4nQHY7pQXV2En//8HWasXM2+xzYlOZbomAykbsKDQp+jI/Q7rZFg/yg9edByotxcP4MG5VNTE+Haa9/ju999jQULtqY7rKyQTKJzegvLzuzqQEzPpjGHQK7Dk5/MZNOm2nSHY7pATU2Yn/zkLeYu28jwL/ncCRjTeDl+Y7qCxoRYvYOTqxSfEKF4QhhfYc8ev5OoqCjIwIF5LFmyjUsvfZVf//p91q+3z9y90WqiIyI/EpF5wKEi8mnCbRXQ0mSfJts1CvlDlVvvf9+aVTPcjh2N/OhHb7Jo2VYOOs9BcL8gjMkWGnYHLQf7x+l3WiOFo8NIMDM+t0SE0tJcBgzI4+2313DBBZO5++5Z1NSE0x1aRmqrRedx4Bzgee++6XaMd8q56XUEJy5sK9nKq28tT3cwppO2bavniiveYNmKbRx8vg9frvaYS+0b07W8s7QaIHdElNIvNZB7YASczEh4HEcYMCCP4uIcHnlkAWef/RzPPLOESMQGLHdEW4mOqmo58BOgJuGGiJSkPjTTI8UcAjnCpPens21bfbqjMR20ZUsd3//+65SvruKgs/wE+sVt8LHJft74HY1B4RHuGVrBfTLn2mCBgMPAgfkEAg633Tadb3/7FdautbNgk9Veiw7ATKDMu5+Z8Nz0VmGH3P3i/N+DU9MdiemAjRt3cvnlr7F+fQ0HnBYgZ3CMeJ0NPja9SMw9JV0C7vidorGZ050F7oDlgQPzWL26mosvfonXXltlwwiS0Gqio6pne/fDVXWEd990G9F9IZqex+3C2pC3kXemrkp3MCYJ69bVcPnlr7FlSx1DJwTJOyBKzJIc00tpRIjXQc5+UUrPaCA0OHPOzmoav5Ob6+e3v53K//7vR9TVRdIdVo/W6qSeInJ0Wy9U1VldH47JGDGHQI7y4NsfM3b0vhQVhdIdkWnF6tU7+MEP3qCmJsx+RwcpHBUmVm9JjuntvNYdn1I8PkzjJh/VswPE6zLjYpm5uX5CoXxeemklc+du4Y9/PImDDuqb7rB6JGmt2UtE3m3jdaqqX0hNSG0bO3aslpWlpufs8nueIOZEcdQmMUyOEg/EGVq/LzddfWq6gzEtWLGiiiuueIP6+giDDgtQPCFCPAzYGVbGJFCcHEChZr6f+pX+jLrUwrZtDcRiyi9/OY6vfOWgrJkhXURmqurYvS2n1RYdVT1lbws32c7twlrprOejGWs5ftx+6Q7IJFiyZBs//OEbRCJxBhwQovi4RjSCJTnGfIZ7ZhaOUjQ6Qu7QGNVlQaLVmdG6U1KSQ2NjlFtvncbHH6/nhhuOs1b2BG1dR+cL3v0FLd26L0TTo8Uc/Dlw72sfUFdn13joKebP38L3v/86sZjSf78gfU9oRBU0akmOMa2KC7E6wV+olJzaSP5hEfBlxtidUMjPwIH5TJmylosuepH587ekO6Qeo6109STv/pwWbmenOC6TQSTs4BsQ5S+PfpzuUAwwe/YmfvjDNxGBkgFB+p4YRnzuBdSMMe0R4o1uC0/+oVFKT2sk0C8zrlvjOMLAgfnU1ka4/PLXefTRBcTjmZGopVJbXVc3evff7b5wTGYSfDFhYWwNs+at5+gj9k13QL3WtGnr+cUv3iUU8lHUN0DxhEacHLVr5RjTUepeWVlCSt+TGmlY46d2fiAj/peKi0OEwzH+8peZTJu2gZtvnkBJSW66w0qbdjsgRaSfiNwlIrNEZKaI/FVE+nVHcCaDxB18QfjrS1NpbMycC3Flk7ffXs1VV71NTo6fwj4B+owPE+gTd8ceGGM6RcNCvE7I2S9KvzMy58rKwaCPQYPymTFjA9/61iuUl+9Id0hpk8xIqyeBLcBXgQu9x0+lMiiTmZyIg/Zr5J4npqU7lF5n8uRl/OpX71NUFKKgwE/R0RFCA+J2GrkxXcK7snLEvbLy7pnRezYRYcCAfKqqGvjOd16hrGxjukNKi2QSnUGqerOqrvJutwADUh2YyUSCE3OYXbuK+Us3pzuYXkFVeeSRBdx888eUlOSQm+sj//AoOftHidWBJTnGdKG4e+0dJ8e9snKfzzXi5PX8mdH79cvFcYQf//gtXnyx981TmEyi84aIXCwijnf7OvB6qgMzmUniDr4A3DF5CuFwz//Fk8lUlfvum81f/jKT0tJcQiGH/EOjFBwaJW4tOcakTNPM6KGBcUq/2EjeIT3/7KzCwiB9+gS58caPuO++2b1qkHJbp5fXiEg18APcea/C3u1J4IruCc9kIicixIobueXhKTYPS4rEYnFuu+0THn54PgMG5BEMOhQcEaHg8AixejLqYmfGZCZvZvQwFBzuTRQ6sGdPJZGT42fAgDweemgev/nNVBoaeseP0bbmuipU1SLv3lFVv3dzVLWoO4M0GUYcJCqUO+uZ9LzNFNLVwuEYN9zwAU8/vZSBA/PwB4TCMRHyDoq6Y3IsyTGm+3jdWeJXio+LUHxCGF9Bz+3O8vsdBg3K5623VnPllW+ybVt9ukNKuaQu+ygifUXkWBH5fNMt1YGZzCbq4MQd3lm/kPdm2MSfXaWhIcq1107h9dfLGTQoH19AKDomQt6IqDsTuSU5xqSFRtzurGBpnH6nN9Lnc40ESmL0xBYe93o7eSxaVMl3vpP9Z2Qlc3r594H3ccfl/K93PzG1YZlsIHEHnwMPffwhK9duS3c4Ga+mJsxPfvIWH320jkGD8nH8UHxsmNz9YzYTuTE9gted5Y3f6XtSmJLTGt3Z0XvYKekiwj775LFtm3tG1syZ2XtGVjItOlcD44DV3vxXRwFVqQzKZA+J+ZCQcvMzb1Bdaxd06axt2+r5wQ9e59NPtzBwYD5OAIqPDxMcFLOzq4zpcXYnPL5cpc+xEfp/uYG8gyI4oZ6V8DSdkfWjH73Jyy+vSHc4KZFMotOgqg0AIhJS1cXAIakNy2QTX9QhnhfhhkmvE4v13L7rnmrjxp1cfvlrrFq1gwED8nCCUDyhkWD/mJ1dZUyPJl6XlqAKBaMilJ7ZQOFRYXyFPeez0D0jK8Tvf/8hf/vb7Kw7iSSZRKdCRIqBycCbIvI8sDqVQZlsI0hE2B7cwR8ffy/dwWSUZcu2c+mlr7Jx40722ScPXw70/XwjgZK4JTnGZJKYe9HBeCPkDo3R77RG+p7YSHCfnjGOJyfHzz775PHgg/P4/e8/JBzOjPm9ktFuoqOqX1HVKlWdCNwAPAycn0zhIvIlEVkiIstF5PoW1l8jIgtF5FMReVtEhnYwfpMhRBwk4rCofi1PvflpusPJCFOmrOHSS19h584w/fvn4eS4c+74Cy3JMSZj6e5urUBJnOIJYUq/3EDR0WFCg2M4OelLeprOyHrllZX87GdvU1MTTlssXSnZs66OFpGrgCOBClVtt/Yi4gPuBc4EDge+ISKHN9tsNjBWVY8EngH+2JHgTWZxcE87f2XFXKYvWJvucHosVeWhhz7lf/5nCrm5fvr2zcGXF6fk5EZ8uUq8wcGSHGMynTdLer2AQM7+MfqMC1N6ZgOlX2qgcEyY0KBYt4/pcRxh0KB8Zs3axPe+9xqbN+/s1v2nQjJnXf0e+BfQDygF/ikiv0ui7GOB5aq60kuMngTOS9xAVd9V1Trv6TRgSEeCN5nHUR8o3Pfe+6zdVJXucHqc+voI118/lfvum0P//nnk5QXwFcbpe3IjTkiJN1qCY0zWiXmtPF5Lj/iU3OEx+ox3W3v6nd5AwagwwQExJJD6xEdEGDgwn9Wra/jOd15l5cqqlO8zlZJp0bkEGKeqN6rqjcDngG8n8brBQOLP9gpvWWu+B7za0goRuUJEykSkbMuWLUns2vRkTtxH3Ilzy7NvsLM+O5pGu8KmTe6g47ffXs2gQfkEAg7B/lFKTm7ECWBJjjG9gqAxt6WnKfFxQkreQTGKjwvT/+wGSs9ooHhCIwWjwuQOixDcJ+ZepLCLp6HYZ59camrCXHrpq8yalbmnn/uT2GY9kAM0nRscAtZ1ZRAi8i1gLHBSS+tV9QHgAYCxY8emf9SW2Wu+qFAfaGDi46/zh++eheMk1YuatebP38LVV7/Lzp0RBg7MQxzIPyRC/mER96wNS3KM6aUEjYLumq1BEb8S6KcE93GfN10oVBwlHhZitUK0WojWOMR2OsTqmxInt7yOKCnJoaYmzJVXvsWtt57A6acP67KadZdWEx0RuRt3KPgOYIGIvOk9Px2YnkTZ64D9Ep4PoYUESUROA34LnKSqjcmHbjKaODgR2CTbuWvyh/z8ghPTHVHavPzyCm666WNCIR/9++fihJSicWFC+8RsSgdjTDOCxoBY07lazT4fHPAVKv5iRZwYGhcSN4vXC7GdQrRWiFULsQbHXVYnxBtp8fOmsDCI3+9w/fXvs2nTTi655HBEMudzqa0WnTLvfibw34TlU5IsewZwkIgMx01wLga+mbiBiBwF/B34kqpuTrJckyVEHCQMs7eW8/wHJZx3wsh0h9StYrE499wzm3/9awH9+uWQk+Mn0C9Gn8+FcQJqVzs2xnSQQBw0Dhrxnu9BwecmQYF+gOxuDcIdE02sQYhsF8KbHaJVPqI73K603Fw/Pl8ud9wxkw0bdnLNNWPx+TKjJb7VREdV/9X0WESCwMHe0yWq7iFsi6pGReSnuFNG+IB/qOoCEbkJKFPVF4DbgQLgaS87XKOq53a6NibjOOIQC8f478JZNMYifO3zozPql0Jn1dSE+e1vp/Lhh+sYMCAPv1/IOzhCwcgoGsU7s8oYY7qSuC1BsbYTodAAJTQo5vaXiRKrcWjc4hDZ6iPYJ4cnn1rM5s113HzzCeTkJDMCJr2kvSsgisjJuGddleMelf2AS1X1/RTH1qKxY8dqWVlZ+xt2wuX3PEHMibpnBpluFdMYEoRD+w7i2gtOJuDr+f88nbV2bTVXXfUOFRU17pWOQ9BnbJjQwBixBoF49id6xphMoYgfJMCu6xpqHHasi9NXC/jFDz7H4YMHpuQHqojMVNWxe1tOMj8b/wx8UVVPUtXPA2cAd+7tjo1J5BMfhIVF29Zz7aQX2Fpdm+6Qulw8rjz33FIuvvglNm7cycCB+QRLlH6nNRAc4E3MaUmOMaZHETSacBZYg6AR6LOvEBlcy51vvsOOup49j2EyiU5AVZc0PVHVpUAgdSGZ3soRByfisC2yk+uffJFPV65Pd0hdZtWqKi6//DVuvXUaubl+SktzyD0gQt+TGxE/xOvtIoDGmAyhgkYcnIiPcGOc2tqefZmQZPoHZorIQ8Bj3vNL2D1Q2ZguJeLgjyphiXLHW+/wlVFjOO/4UekOq9PC4RiTJs3n4Yfn7briqBOEomPC5OxrXVXGGJNqySQ6VwI/Aa7ynk8F7ktZRMaI4MNHrDHGcwtms3xjJVefdwJ+X2aNnZozZzMTJ35ERUUNpaU5BIIOoX1jFI6O4ITsrCpjjOkObSY63nxVc1X1UOCO7gnJGJfP8RFvjDN362p+9UgVN3ztdIoL8tIdVrtqasLcffcsnntuGbm5PgYNyiO4T4zCIxvxFcbdCwDW21lVxhjTHdr8tFXVGLBERPbvpniM2YMjDk7YYUu4ml8+/jyL1/bcyy2pKu++u4bzz5/Mf/+7jH32yaV0WJC+nw/Td0IEJ0+J17sD+4wxxnSPZLqu+uJeGXk6sGsaU7vejekuIg6+iFLvRPnDq29w0VFj+dK4Q9Md1h42b97J//3fJ7z/fgVFRUEGH5BLwagIoX1joEKsHqybyhhjul8yic4NKY/CmPaI4Fcf0XCMJ2bPYP6ajVxyylEMKumT1rC2bq3j9dfLue++OUQicfYdmkfB4VHyhruzmcTrbRyOMcakU1tzXeXgDkQ+EJgHPKy6e1oxY9LBLz5ijXE+3bqWec+spZ9TyJeOOJwvjD2g2wYr19dH+PDDdTz77DJmztxIPA4l/YOUjPSRf2gjOEq8weaoMsaYnqCtFp1/ARHcs6zOBA4Hru6OoIxpi89xIAoaj7PVX8O/53zC42UzGNl3CF898QhG7FfS5fuMxeLMmbOZF19cwRtvrCYajRMICIMOyCE0ME7BoVEkoO4s43EbaGyMMT1FW4nO4ap6BICIPExyM5Yb023EcfDFgTjEJca8mjXMe3kNuTvzOH3UoZz1+YPJzd27a1uuWlXFa6+t4rnnllFdHcYJKgMOC5I/xD1VXPxhEEXDdiaVMcb0RG0lOrsm7vQm6OyGcIzpHEd9bisPcRoL6nixfBbPzZ/FiNAgvvr5kYw5YiAAsZgSjcaJRuPEYkokEmthWZz587fwn/8sZeWqKnL7Q+nhPvYd7uAvUrwdEQ+DNtgYHGOM6cnaSnRGi0i191iAXO+5AKqqRSmPzpgOEtxuLUEJ5itrZAN3fLiBumcg2qjuzOBhIR6BeMSdnFebZvONya7HTq5ScqjDEac5iANI3Jvvxd2LMcaYzNBqoqNqU3ibTCZITHD/iOP0GYI7u64jTasRUXcy3ri3oInibhf3Wm3UWm2MMSZTJXN6uTEZzkEjbv6yJ0tejDEm29noSWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNZKaaIjIl8SkSUislxErm9hfUhEnvLWfyIiw1IZjzHGGGN6l5QlOiLiA+4FzgQOB74hIoc32+x7wHZVPRC4E/hDquIxxhhjTO/jT2HZxwLLVXUlgIg8CZwHLEzY5jxgovf4GeAeERFV1RTG1SYFlHi6dm+MMcZkDkl3AO1LZaIzGFib8LwCGN/aNqoaFZEdQD9ga+JGInIFcIX3tFZEliSsLm2+fWcVlO57mD/HH+qKsnqLxtpaCRUUpC0x7e3s+KefvQfpZcc/vRpqauSxH313nmosmoLih3ZFIalMdLqMqj4APNDSOhEpU9Wx3RyS8YhIWd32Sjv+aWLHP/3sPUgvO/7plQnfwakcjLwO2C/h+RBvWYvbiIgf6ANUpjAmY4wxxvQiqUx0ZgAHichwEQkCFwMvNNvmBeBS7/GFwDvpHJ9jjDHGmOySsq4rb8zNT4HXAR/wD1VdICI3AWWq+gLwMPCoiCwHtuEmQx3VYpeW6TZ2/NPLjn/62XuQXnb806vHH3+xBhRjjDHGZCu7MrIxxhhjspYlOsYYY4zJWhmT6Nh0EumVxPG/TES2iMgc7/b9dMSZrUTkHyKyWUTmt7JeROQu7/35VESO7u4Ys1kSx/9kEdmR8Pf/++6OMZuJyH4i8q6ILBSRBSJydQvb2P9AiiR5/Hvs/0BGXEcnYTqJ03EvPDhDRF5Q1cSrLO+aTkJELsadTuKi7o82+yR5/AGeUtWfdnuAvcMk4B7gkVbWnwkc5N3GA3/jsxfoNJ03ibaPP8BUVT27e8LpdaLA/6jqLBEpBGaKyJvNPoPsfyB1kjn+0EP/BzKlRWfXdBKqGgaappNIdB7wL+/xM8CpIpIBF6fOCMkcf5NCqvo+7pmJrTkPeERd04BiERnUPdFlvySOv0khVd2gqrO8xzXAItwr6yey/4EUSfL491iZkui0NJ1E84O8x3QSQNN0EmbvJXP8Ab7qNRk/IyL7tbDepE6y75FJneNEZK6IvCoiI9MdTLbyhiUcBXzSbJX9D3SDNo4/9ND/gUxJdEzP9yIwTFWPBN5kd+uaMb3BLGCoqo4G7gYmpzec7CQiBcCzwM9VtTrd8fQ27Rz/Hvs/kCmJjk0nkV7tHn9VrVTVRu/pQ8Ax3RSbcSXzP2JSRFWrVbXWe/wKEBCR0jSHlVVEJID7JftvVX2uhU3sfyCF2jv+Pfl/IFMSHZtOIr3aPf7N+sLPxe3DNd3nBeA73pknnwN2qOqGdAfVW4jIwKYxgSJyLO5nq/3Q6iLesX0YWKSqd7Symf0PpEgyx78n/w9kxFlX3TidhGlBksf/KhE5F3d0/jbgsrQFnIVE5AngZKBURCqAG4EAgKreD7wCfBlYDtQB301PpNkpieN/IfAjEYkC9cDF9kOrS00Avg3ME5E53rLfAPuD/Q90g2SOf4/9H7ApIIwxxhiTtTKl68oYY4wxpsMs0THGGGNM1rJExxhjjDFZyxIdY4wxxmQtS3SMMcYYk7Us0THGdJg3W/2+SWw3SUQu7ET5V4rId1pYPqxpBnERGSMiX05YN1FErk2ibBGRd0SkqKNxtVDWWyLSd2/LMcakjiU6xpjOuAxoN9HpLFW9X1XbmikcYAzudVM66svA3C6aQuBR4MddUI4xJkUs0TGml/NaSRaLyL9FZJE3KWuet+4YEXlPRGaKyOsiMshroRkL/FtE5ohIroj8XkRmiMh8EXmg6QqprexvHxGZ6T0eLSIqIvt7z1eISF5i64wXw1wRmQv8xFsWBG4CLvJiuMgr/nARmSIiK0XkqlZCuAR4PiGe73iT0c4VkUe9ZZNE5G8iMs0r62QR+Yd3fCYllPUC8I0OHnJjTDeyRMcYA3AIcJ+qHgZUAz/25ra5G7hQVY8B/gHcqqrPAGXAJao6RlXrgXtUdZyqjgJygbNb25GqbgZyvK6jE72yThSRocBmVa1r9pJ/Aj/zJgtsKiMM/B54yovhKW/VocAZwLHAjV4dmpsANCVaI4HfAV/wyr86Ybu+wHHAL3ATmjuBkcARIjLGi2M7EBKRfq3V1xiTXpboGGMA1qrqh97jx4ATcJOfUcCb3mXff4c7UWJLThGRT0RkHvAF3ISgLR/hJhyfB/6fd38iMDVxIxEpBopV9X1v0aPtlPuyqjaq6lZgMzCghW1KVLXGe/wF4Glve1R1W8J2L3qXsJ8HbFLVeaoaBxYAwxK220wKu/GMMXsnI+a6MsakXPO5YBQQYIGqHtfWC0UkB7gPGKuqa0VkIpDTzv7ex01shuJ2I/3K2+fLHQ99D40Jj2O0/BkXFRHHS1qSKSverNx4s3JzcOf2Mcb0QNaiY4wB2F9EmhKabwIfAEuA/k3LRSTgdfUA1ACF3uOmpGariBTgTu7XnqnAt4BlXsKxDXeQ8AeJG6lqFVAlIid4iy5JWJ0YQ0csAUZ4j98BvtbU9SQiJR0pyBuLNBAo70QcxphuYImOMQbcL/+fiMgi3LEpf/PGwVwI/MEbCDwHON7bfhJwv9el1Qg8CMzHneF+Rns7U9Vy3Bajpi6pD4Aqb8xLc98F7vX2lTjI+V3cwceJg5GT8TLuTOSo6gLgVuA9r453dKAcgGOAaaoa7eDrjDHdxGYvN6aXE5FhwEveQOKsJyKDgEdU9fQuKOuvwAuq+vbeR2aMSQVr0THG9CqqugF4sCsuGAjMtyTHmJ7NWnSMMcYYk7WsRccYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLX+6A+goEbkMGJbmMIwxxhiTQqo6sSvKEVXtinKMMcYYY3oc67oyxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkrf8P/bbNq5a8WG0AAAAASUVORK5CYII=\n", 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xWwIaRORY3BaWtmwB4rjjZrrC94AvqOrOxIVJHrfHcQehX8Lubitwj9+VXmuPiEi+uIOuC1sKQEQOEZEveGNWGnATzXh7gYs7KPoYYDKwHfhnC9uM8+II4I6HakgoexN7Hsd83ORni/fa7+K26LTL6w57FbhPRPqKSEBEmsZTdeh4GJNulugY43pRRGpwf63+FveaON9t+yWtU9XFuONTVnrN+y016+fhdo9U4Q4eHop7xg2quga3ZeJ/cLsw5gCjvdf9DPdLbiVui9PjuINDW/MrYDkwTUSqgbeAQzpbN0/TWVJNt2u85T8GbvKO5e9xk4tWed0lt+KeTl0lIns1zkNVV6hqWSur2zxuqvqJt35f3C/5puVlwA9wu4y24x7Ly9oII4Q7AHwrbtfQPsCv29j+l97xqsTtcpsJHN88WfMU4SYa23G73iqB2711D+OOC6oSkcmquhB3HNLHuEnQEcCHbcTR3LdxW8EWA5txu+M6czyMSStR7bKWY2OMMcaYHsVadIwxxhiTtSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZK1UTfiXMqWlpTps2LB0h2GMMcaYFJo5c+ZWVW3x4qYdkXGJTmVJH7bd+qt0hwFA/IyvpTsEY4wxJiuJyOr2t2qfdV0ZY4wxJmtZomOMMcaYrJURiY6IXCEiZSJSxo7qdIdjjDHGmAyREYmOqj6gqmNVdSx9Wp2o1xhjjDFmDxmR6BhjjDHGdIYlOsYYY4zJWpboGGOMMSZrZUSiY4ORjTHGGNMZGZHo2GBkY4wxxnRGRiQ6xhhjjDGdYYmOMcYYY7KWJTrGGGOMyVqW6BhjjDEma2VEomNnXRljjDGmMzIi0bGzrowxxhjTGRmR6BhjjDHGdIY/3QF01DFFfSk742vpDsMYY4wxGUBUNd0xdIiI1ABL0h1HGpUCW9MdRJr05rpD765/b6479O769+a6Q++u/yGqWri3hWRciw6wRFXHpjuIdBGRst5a/95cd+jd9e/NdYfeXf/eXHfo3fUXkbKuKMfG6BhjjDEma1miY4wxxpislYmJzgPpDiDNenP9e3PdoXfXvzfXHXp3/Xtz3aF3179L6p5xg5GNMcYYY5KViS06xhhjjDFJsUTHGGOMMVmrRyU6IvIlEVkiIstF5PoW1odE5Clv/SciMixh3a+95UtE5IxuDbwLdLbuIjJMROpFZI53u7/bg+8CSdT/8yIyS0SiInJhs3WXisgy73Zp90XdNfay7rGE9/6F7ou66yRR/2tEZKGIfCoib4vI0IR12f7et1X33vDeXyki87w6fiAihyesy/bP/Bbr3ls+8xO2+6qIqIiMTVjWsfdeVXvEDfABK4ARQBCYCxzebJsfA/d7jy8GnvIeH+5tHwKGe+X40l2nbqr7MGB+uuvQDfUfBhwJPAJcmLC8BFjp3ff1HvdNd526o+7eutp016Eb6n8KkOc9/lHC335veO9brHsveu+LEh6fC7zmPe4Nn/mt1b1XfOZ72xUC7wPTgLGdfe97UovOscByVV2pqmHgSeC8ZtucB/zLe/wMcKqIiLf8SVVtVNVVwHKvvEyxN3XPBu3WX1XLVfVTIN7stWcAb6rqNlXdDrwJfKk7gu4ie1P3bJBM/d9V1Trv6TRgiPe4N7z3rdU9GyRT/+qEp/lA09kzWf+Z30bds0Ey33kANwN/ABoSlnX4ve9Jic5gYG3C8wpvWYvbqGoU2AH0S/K1Pdne1B1guIjMFpH3ROTEVAebAnvz/vWG974tOSJSJiLTROT8Lo2se3S0/t8DXu3ka3uavak79JL3XkR+IiIrgD8CV3XktT3Y3tQdesFnvogcDeynqi939LXNZeIUEGZPG4D9VbVSRI4BJovIyGa/Bkz2Gqqq60RkBPCOiMxT1RXpDioVRORbwFjgpHTH0t1aqXuveO9V9V7gXhH5JvA7IOPGYnVWK3XP+s98EXGAO4DLuqK8ntSisw7YL+H5EG9Zi9uIiB/oA1Qm+dqerNN195rvKgFUdSZuf+XBKY+4a+3N+9cb3vtWqeo6734lMAU4qiuD6wZJ1V9ETgN+C5yrqo0deW0Ptjd17zXvfYIngfM7+dqeptN17yWf+YXAKGCKiJQDnwNe8AYkd/y9T/egpIRBR37cwYTD2T04aWSzbX7CngNy/+M9Hsmeg5NWklkD0/am7v2b6oo7sGsdUJLuOnV1/RO2ncRnByOvwh2M2td7nDH138u69wVC3uNSYBktDOjrybck//aPwv0wP6jZ8qx/79uoe2957w9KeHwOUOY97g2f+a3VvVd95nvbT2H3YOQOv/dpr3CzynwZWOr9Y//WW3YT7i8ZgBzgadzBR9OBEQmv/a33uiXAmemuS3fVHfgqsACYA8wCzkl3XVJU/3G4fbE7cVvxFiS89nLvuCwHvpvuunRX3YHjgXneP/084HvprkuK6v8WsMn7G58DvNCL3vsW696L3vu/Jny+vUvCl2Ev+Mxvse695TO/2bZT8BKdzrz3NgWEMcYYY7JWTxqjY4wxxhjTpSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtSzRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtSzRMcZ0mojcLyI3dFFZ+4tIrYj4vOdTROT7XVG2V96rInJpV5VnjMkM/nQHYIzpuUSkHBgARIEYsBB4BHhAVeOqemUHyvm+qr7V2jaqugYo2NuYvf1NBA5U1W8llH9mV5RtjMks1qJjjGnPOapaCAwFbgN+BTzclTsQEfvRZYxJCUt0jDFJUdUdqvoCcBFwqYiMEpFJInILgIiUishLIlIlIttEZKqIOCLyKLA/8KLXNfVLERkmIioi3xORNcA7CcsSk54DRGS6iFSLyPMiUuLt62QRqUiMT0TKReQ0EfkS8BvgIm9/c731u7rCvLh+JyKrRWSziDwiIn28dU1xXCoia0Rkq4j8NrVH1xiTKpboGGM6RFWnAxXAic1W/Y+3vD9ud9dv3M3128Aa3JahAlX9Y8JrTgIOA85oZXffAS4HBuF2n92VRHyvAf8PeMrb3+gWNrvMu50CjMDtMrun2TYnAIcApwK/F5HD2tu3MabnsUTHGNMZ64GSZssiuAnJUFWNqOpUVdV2ypmoqjtVtb6V9Y+q6nxV3QncAHy9abDyXroEuENVV6pqLfBr4OJmrUn/q6r1qjoXmAu0lDAZY3o4S3SMMZ0xGNjWbNntwHLgDRFZKSLXJ1HO2g6sXw0EgNKko2zdvl55iWX7cVuimmxMeFxHFw2UNsZ0L0t0jDEdIiLjcBOdDxKXq2qNqv6Pqo4AzgWuEZFTm1a3Ulx7LT77JTzeH7fVaCuwE8hLiMmH22WWbLnrcQdXJ5YdBTa18zpjTIaxRMcYkxQRKRKRs4EngcdUdV6z9WeLyIEiIsAO3NPR497qTbhjYTrqWyJyuIjkATcBz6hqDFgK5IjIWSISAH4HhBJetwkYJiKtfcY9AfxCRIaLSAG7x/REOxGjMaYHs0THGNOeF0WkBrcb6bfAHcB3W9juIOAtoBb4GLhPVd/11v0f8DvvjKxrO7DvR4FJuN1IOcBV4J4BBvwYeAhYh9vCk3gW1tPefaWIzGqh3H94Zb8PrAIagJ91IC5jTIaQ9scKGmOMMcZkJmvRMcYYY0zWskTHGGOMMVnLEh1jjDHGZC1LdIwxxhiTtTJuIr3S0lIdNmxYusMwxhhjTArNnDlzq6r2b3/LtmVcojNs2DDKysrSHYYxxhhjUkhEVre/Vfus68oYY4wxWcsSHWOMMcZkLUt0jDHGGJO1Mm6MjjHGGLM3IpEIFRUVNDQ0pDsUA+Tk5DBkyBACgUBKyrdExxhjTK9SUVFBYWEhw4YNw52D1qSLqlJZWUlFRQXDhw9PyT6s68oYY0yv0tDQQL9+/SzJ6QFEhH79+qW0dc0SHWOMMb2OJTk9R6rfC0t0jDHGGJO1bIyOMcaYXu2CCyZTXl7dZeUNG1bEc8+d3+r6yspKTj31VAA2btyIz+ejf3/3AsDTp08nGAy2+tqysjIeeeQR7rrrrk7FNmnSJMrKyrjnnnta3WbKlCkEg0GOP/74Tu2jJVVVVTz++OP8+Mc/7rIyk2WJjjHGmF6tvLyawYMLurS8tvTr1485c+YAMHHiRAoKCrj22mt3rY9Go/j9LX89jx07lrFjx3ZZrC2ZMmUKBQUFXZ7o3HfffWlJdKzrqhtE4zGWbNnA2ysXsr56O6qa7pCMMcb0IJdddhlXXnkl48eP55e//CXTp0/nuOOO46ijjuL4449nyZIlgJuEnH322YCbJF1++eWcfPLJjBgxotVWnn/+858cfPDBHHvssXz44Ye7lr/44ouMHz+eo446itNOO41NmzZRXl7O/fffz5133smYMWOYOnVqi9sBvPfee4wZM4YxY8Zw1FFHUVNTA8Dtt9/OuHHjOPLII7nxxhsBuP7661mxYgVjxozhuuuuS9lxbIm16KTQjoZ6Pt20lpnry2mIRlGUGevKKc0r4HNDRnBw6UCCPnsLjEm1xmiEpVs3snjrRnIDAYpCeRSGcsgLBMgNBMkLBMnzB8kJBHFskKpJk4qKCj766CN8Ph/V1dVMnToVv9/PW2+9xW9+8xueffbZz7xm8eLFvPvuu9TU1HDIIYfwox/9aI/r0WzYsIEbb7yRmTNn0qdPH0455RSOOuooAE444QSmTZuGiPDQQw/xxz/+kT//+c9ceeWVe7Qybd++vcXt/vSnP3HvvfcyYcIEamtrycnJ4Y033mDZsmVMnz4dVeXcc8/l/fff57bbbmP+/Pm7WrK6U0q/ZUXkS8BfAR/wkKre1sI2XwcmAgrMVdVvpjKmVIursmZHJTPWrWLFts0A5PqDFAZC1NZGyM3zs6OhnpeXzuX15fM5atBQxgzan5Lc/DRHbkx2UVU276xhzobVfLq5glg8jk8cFIhrHBQcRxAE9bZXlBx/gPxgiIJgDoMK+jB28DAKgjnpro7pBb72ta/h8/kA2LFjB5deeinLli1DRIhEIi2+5qyzziIUChEKhdhnn33YtGkTQ4YM2bX+k08+4eSTT941Buiiiy5i6dKlgJtYXXTRRWzYsIFwONzqdWxa227ChAlcc801XHLJJVxwwQUMGTKEN954gzfeeGNXMlVbW8uyZcvYf//9u+YgdULKEh0R8QH3AqcDFcAMEXlBVRcmbHMQ8GtggqpuF5F9UhVPqtVHwizYvJ5PKlZSG25ABAqCOTgibNvWwIrl22kMxwEoLAzQr18uhX0cplesZPq6VQztU8KxQ0YwrLgUn2M9isZ0VjgWZcnWjcxYt4rNO92xEvmBEL5A+/9XqkpMlZ3hRqob6lldtZUZ61YxbvBwjh0ygrxA64NEjdlb+fm7f/DecMMNnHLKKfz3v/+lvLyck08+ucXXhEKhXY99Ph/RaDTp/f3sZz/jmmuu4dxzz2XKlClMnDixQ9tdf/31nHXWWbzyyitMmDCB119/HVXl17/+NT/84Q/3KKO8vDzpuLpaKlt0jgWWq+pKABF5EjgPWJiwzQ+Ae1V1O4Cqbk5hPF1OVdlYW83M9eUs3LKeuMYJ+QIUhtxff3V1UVasqKKqqhG/TwiFfKgqO3dGqa7egYjgOEKf4iA7+jWyfMsW+uTnMn7IcEbtM4T8YKidCIwxTTbXVjNn4xo+3VRBLB7D7/goDOZ06BodIoJfBHDAB7lALB5nWsUKytaXM37ICMYNHk6OPzWXqjemyY4dOxg8eDDgninVWePHj+fqq6+msrKSoqIinn76aUaPHv2ZffzrX//a9ZrCwkKqq3cPqG5tuxUrVnDEEUdwxBFHMGPGDBYvXswZZ5zBDTfcwCWXXEJBQQHr1q0jEAhQWFi4awxPd0tlojMYWJvwvAIY32ybgwFE5EPc7q2Jqvpa84JE5ArgCiCtzV+J6iNh/rNgBhtqqhCEvEBwV0tMNBJn9ZpqNqzfiQjkhBzwPmxFhEBACHi/LuNxpWp7I9srG0EgmLuDFWu2UVgwj+OHH8CZh46yC1sZ04pwLMrSrZuYsX4lm2qrEYTcQBB/F7a8+ByHolAu0XiMD9csY3rFSo7f70CO3ncoIUt4ssKwYUVdfnr53vrlL3/JpZdeyi233MJZZ53V6XIGDRrExIkTOe644yguLmbMmDG71k2cOJGvfe1r9O3bly984QusWrUKgHPOOYcLL7yQ559/nrvvvrvV7f7yl7/w7rvv4jgOI0eO5MwzzyQUCrFo0SKOO+44AAoKCnjsscc44IADmDBhAqNGjeLMM8/k9ttv7/zB6SBJ1RlAInIh8CVV/b73/NvAeFX9acI2LwER4OvAEOB94AhVrWqt3LFjx2pZWVlKYk5WNB7jiXmfsL66ioJgaFcioqps2LCT8vJq4jElGHQQpwNJiiqxOESjcRDI7SOce/QRnDdmdIpqYkzmqtixjWcXzqQ+Gibg+MjxB7rlR0E0HqMuEibo83PC0IMYM3B/O6kgwyxatIjDDjss3WGYBC29JyIyU1X3+lz6VP53rgP2S3g+xFuWqAL4RFUjwCoRWQocBMxIYVx7RVV5bdk8Kqq3U5TQLL59ewMrVlRRXx8jEHB2tdh0iAg+H7sGozVUx3hh1jwKAiFOHXloV1bDmIy2YPM6Xloyl4DPR1Eot1v37XfcfUZiMd5ZuYiP1iznxKEHc+SA/Qh4/7vGmJ4jlaNeZwAHichwEQkCFwMvNNtmMnAygIiU4nZlrUxhTHtt2toVzNtUsavvv74+yvz5W5k/v5JwOE5Ojg+fr2t+VQYDPhprlMc/KePjpau6pExjMpmq8n75El5YPIccfyCtY2USk6w3VyzgbzPeYeGWdXadLGN6mJS16KhqVER+CryOO/7mH6q6QERuAspU9QVv3RdFZCEQA65T1cpUxbS3Fm3ZwJTyJbvOplq1agfr1tUCe47D6UoBv4/G2hgPTf2IXH+AMSOGtP8iY7JQJBbjlaVzWbhlPQXBnB5zdmLQ5yfo8xOORXl+0WzWDKrk1BEjrXXHmB6i3URHRP6Ml6R0tHBVfQV4pdmy3yc8VuAa79ajra/ezotL5pAbCOBzHNauraGiooZQ0NexcTidEPD5CNfHuPvt97jWfxqH7T8gpfszpqepDTfy7IIy1tdWURTK7ZED9IM+P37Hx+wNa1lfU8UFh42lODcv3WEZ0+sl85NoEfCAiHwiIleKSJ9UB9XT7Gio4z8LZuATIejzs6OqkdXl1QS7Iclp4hcfkUic2195i5Xre2yjlzFdbuvOGibN/oCNO3fsMS6uJ3JEKArlsLVuJw/Pep9llZvSHZIxvV67iY6qPqSqE4DvAMOAT0XkcRE5JdXB9QQN0QhPzZ9OJBYjNxAkHI6xcFElPr97DZzu5FMfUY1zy39fp2LTjm7dtzHpsHLbFibN+ZD6SLjD18RJFxGhIBjCEYdnF5bx7qrFxOLxdIdlTK+V1Bgd7yrHh3q3rcBc4BoR+aGqXpzC+NIqFo8zedEsttXXURTKQVVZtLCSWEwJhdLT/+6L+4j6Y9zwxCv83yVnM7B/YVriMCaVVJXZG9bwxor5hHwBQoHMO3075PfjdxymrV3BuurtnH/YUTaVRA/19xlT2FZf22XlleQW8MNxJ7e6vrKyklNPPRWAjRs34vP5dk3RMH36dILBtq8DNWXKFILBYIuzi0+aNInrrruOIUOGUFtby4gRI7jxxhvbnYl88uTJHHzwwRx++OHt1C555eXlfPTRR3zzm+md2andFh0RuRNYDHwZ+H+qeoyq/kFVzwGOSnWA6aKqvLVyIau2b6XQu0LxqlXVVNeECQXTOwjSF3WI50W5/p+vsLWyLq2xGNPVYvE4b61cyGvL55EXCBLyZ16S08S92GAO62q28/DMqaypsm7nnmhbfS19cvK67NZe0tSvXz/mzJnDnDlzuPLKK/nFL36x63l7SQ64ic5HH33U6vqLLrqI2bNns2zZMq6//nouuOACFi1a1GaZkydPZuHChW1u01Hl5eU8/vjjXVpmZyTzjf0pMEZVf6iq05utOzYFMfUIZevLmbl+NYUht7m8srKedetq3JactDefC07EQYvDXHv/S1RVNaQ5HmO6RkM0wrMLyyhbV05RKBe/k/lnLokIhcEcYhrn8XnTmLZ2BXE7Bd00M3PmTE466SSOOeYYzjjjDDZs2ADAXXfdxeGHH86RRx7JxRdfTHl5Offffz933nknY8aMYerUqW2We8opp3DFFVfwwAMPAPDggw8ybtw4Ro8ezVe/+lXq6ur46KOPeOGFF7juuusYM2YMK1asaHE7gKeffppRo0YxevRoPv/5zwMQi8W47rrrGDduHEceeSR///vfAXcurKlTpzJmzBjuvPPOVB26diWT6HxLVXcmLhCRtwFUNSsHiiyv3MTbKxdSEAzieNfKWbJkG4GA04PGCHjJTv9Grr7zJXbssGTHZLbGaIQnPp3Gyu1bKAq5l3DIJjn+AHmBEO+uWsyzC8qoj4TTHZLpIVSVn/3sZzzzzDPMnDmTyy+/nN/+9rcA3HbbbcyePZtPP/2U+++/n2HDhu3RCnTiiSe2W/7RRx/N4sWLAbjggguYMWMGc+fO5bDDDuPhhx/m+OOP59xzz+X2229nzpw5HHDAAS1uB3DTTTfx+uuvM3fuXF54wb003sMPP0yfPn2YMWMGM2bM4MEHH2TVqlXcdtttnHjiicyZM4df/OIXKTp67Wu1TVhEcoA8oFRE+gJNnzpFuPNYZaVNtdX8d/EscvwB/I6PeFxZuLASVfD5esZ1O3YTnIgQH1LPVbe9zD2/OYfCQptd2WSeWDzO5MWz2bSzOmMGHXeG3+vKWrF9M/+YNZULR45lQEGvO5HVNNPY2Mj8+fM5/fTTAbeFZNCgQQAceeSRXHLJJZx//vmcf/75nSo/8SKW8+fP53e/+x1VVVXU1tZyxhlntPia1rabMGECl112GV//+te54IILAHjjjTf49NNPeeaZZwB3EtBly5Yl1Q3XHdr65v4hMBN3APIs7/FM4HngntSH1v1qGut5av50BNk1d82yZdupq4sSDPbUJnQHJyLo8Dp+86c37aqsJuM0jYdbuW1zVic5TUSEolAu9dEIj8z5iEVb1qc7JJNmqsrIkSN3jdOZN28eb7zxBgAvv/wyP/nJT5g1axbjxo0jGo12uPzZs2fvmkfqsssu45577mHevHnceOONNDS03BvQ2nb3338/t9xyC2vXruWYY46hsrISVeXuu+/eFf+qVav44he/2Mmj0fVaTXRU9a+qOhy4VlWHJ9xGq2pWJjovL/2U+kiYPG/m440bd7J5cx2hUE9ryWlGHfxxYXv/Sia/vCTd0RjTIdPXrWLW+tUU9tALAaZKXiBIwOdj8qLZTFm1mLjaKei9VSgUYsuWLXz88ccARCIRFixYQDweZ+3atZxyyin84Q9/YMeOHdTW1lJYWEhNTU1SZb/33ns88MAD/OAHPwCgpqaGQYMGEYlE+Pe//71ru+ZltrbdihUrGD9+PDfddBP9+/dn7dq1nHHGGfztb38jEokAsHTpUnbu3NmhOFOpra6rL6jqO8A6Ebmg+XpVfS6lkaXBjsb6XWd41NZGWL68ikDAlxEfvhpzCOTAv6eVcfy4IQwYUJDukIxp1+ItG3h31SLyg6GsG5OTjKDPj08cPl67gk07qznv0KPSOn9Xb1WSW9Dlp5d3hOM4PPPMM1x11VXs2LGDaDTKz3/+cw4++GC+9a1vsWPHDlSVq666iuLiYs455xwuvPBCnn/+ee6+++7PjNN56qmn+OCDD6irq2P48OE8++yzu1p0br75ZsaPH0///v0ZP378rkTk4osv5gc/+AF33XUXzzzzTKvbXXfddSxbtgxV5dRTT2X06NEceeSRlJeXc/TRR6Oq9O/fn8mTJ3PkkUfi8/kYPXo0l112WdrG6UhrXR0i8r+qeqOI/LOF1aqql6c2tJaNHTtWy8rKUlL238umUB8J46jDrFmbiEbjBAI9tcuqJUo8EKdoXQl3TfxyRiRopvdaX72dxz6dRtDn29VV3FupKrXhRvrk5PK1kePol2c/VFJp0aJFu774Tc/Q0nsiIjNVdezelt3qp4uq3ujdf3dvd5JplizZTrgxTignk5IcAMEXF7b1q+T5l5dy/tmHpDsgY1q0rX4nT82fjk+cbklyVKGxMUokHMfndwgEHPx+J/1XivCICIWhHGrCDUya/QHnH3Y0B5Tsk+6wjMkKyUzqeTXwT6AGeBA4GrheVd9IcWxpsWH9TrZtqycnTVc+3lsacwjmwaMfzOD4Y4ewzz756Q7JmD3URcI8NW860XicfO9inF0lEonTUB+loSFKXX2U2toIdXURGhtj7mmjXmKjincmpRAIOASDPoJB9z4n5CMQ9OH3O4RCPvLy/N3WOpofCNEYjfL0ghmcMuxQjh0ywlpmjdlLyfyUulxV/yoiZwD9gG8DjwJZl+hUbW+koqLWPcMqkz9cGoWCEXFuvvs97rrpTPugND1GJBbj2QVlVDfWUxjau+kQwo0xtmytp6YmTF1dhPr6KKruv+7uRMYd/xAMtnANLFVUIRZT6nZGqK1V73WKIIgDeD37hUVB+vbNoagoSGFhMKXz3DVNHfHOqsVs2lnNmQcdScCXmT+8ejJVtc/GHiLVZwsnk+g0/SV8GXhEVRdIlv51LFu+HcdHFgyKFHwqVJZs5YWXl3He2QenOyBjiKvyyrK5VFRvp2gvkpyqqkbWratl+/YGVBXHcSfY7fAFPUUQAR+Ar/XXqSq1tRF27AjjiJv75OcH6Ns3RJ8+IQoLg/j9XXtmZtPUEQs3r2drXQ1fPXwcfXJyu3QfvVlOTg6VlZX069fPkp00U1UqKyvJyUndPHDJJDozReQNYDjwaxEpBLLyPMh4THESfsVlMo06hAph0pTpHHfsYOvCMmk3tXwJCzdvoCjU8WvlRCJxNm+uY11FDeFIHBFabqVJAREhEBAC3slQqkpDQ5S1ayNUVNSCQk6uj+LinF3JT1ckPk3jdrbW1fLP2e7FBYcUlex1uQaGDBlCRUUFW7ZsSXcoBjfxHDJkSMrKb/Wsq10biDjAGGClqlaJSD9gsKp+mrKo2pDKs64uv+cJYk4UR7OkmViUuD9On9X9+estZ9gvF5M2czas4dVl8ygIhvA5yScBNdVh1m+oZcuWelDw+wVfF7ee7DVVYjElGvM+SxUKCgKU9s+lb98c8vP3/nTxhmiESCzGKcMPZezgYTjSw46BMSmQ8rOumqhqXEQ2AYeLSO8+BzTTqOBD2FKymRdfXs65Zx+U7ohML7Ry2xZeXz6f/GAwqSQnFlO2bKmjoqKWhvooCAQDDpLCcTF7RQSfX2g6eUxVqa+PsmrVDsrLq/H5hJKSHEpLczvd2uNOSePw9qpFLNm6kXMOGUNxbl4XV8SY7JTMWVd/AC4CFgIxb7EC76cwLtNFNOKQ2wf+8dYnfO7Yfa0Ly3SrzbXVPLdoJkGfr92ZyGMxZfXqajZsqEXj4POLe1XyDGuJFBH8AcEfcBOaeFzZsqXebZXCHd/Tv38uxcU5FOQHdo+CbIff8VEUzGFDbRUPznyP0w8YyeiB+1lLrTHtSKaF5nzgEFVtTHEsJkU0LPQ5PM4tf5nKX2+1LizTPeoiYZ5eMANQQv62J/fbtq2BpUu3E4nECQadlJ7V1N0cRwh5l6tQVRrqo6xaVY1INY4DRUUhSkpyKCoMkp8faLPlSkQoCOYQicV4ddk8llRu5MsHHbnXZ7AZk82SSXRWAgHAEp1MpYJfhM3Fm3np5eWcY11YJsXiGueFxbOpDTe2+SUcicRZsaKKLVvq8PsdcjLuIp0ds7u1x30ejyvV1Y1s395IU35TUBh0E582TmUP+HwUOTmUV23lwZnvceZBR3Bo6SD7EWNMC5JJdOqAOSLyNgnJjqpelbKoTJeLhx1y+8GDr33CeOvCMin2weplrNq+tc3TyLdsrmP58ipiMSUUyow55bqae2q8b48zunbujFBdveep7G7iEyI/z0/Qax0SEQqDOYRjUSYvms1h/TdyxoGjyA203XpmTG+TTKLzgnczma5R6HtEnFvu/IC//r8v9sovFpN6yys38eHa5RQEQy3+jTU2xli2bDvbtzcQCDiEMmo+udRq7VT2NWtqEHEnVRQgN9dPfkGAgoIgubl+cnL8LNqygdU7Kjn74NE2fYQxCZI56+pfIpIL7K+qS7ohJpMqKvh9wsbCTbz08grOOfvAdEdkssy2+p08v3g2Ob7AZ8+wUli/oZZVq3agijvNiiXbbRIR/H7Bn/BJrao0hmPUb42yeXPdrgucqkJOgbBk5TsM0BLGlR7AgNJ8SkpyKCnJpagoaD9uTK+UzFlX5wB/AoLAcBEZA9ykquemODaTAvFGIX+AwwMvTmP8sYOsC8t0mXAsyrMLyoipkhvY86Olri7K0qXbqKkJEwj48LVxJWLTtqbkpzlVJdoI1Q0RakMbWbJ5E1tnQHW5EIsoIlBc7J7mPmBAPoMG5bPvvgWUlrrX+ykpyaG4OERxcYiAtbKZLJJM19VE4FhgCoCqzhGRESmMyaSUQBj6HhXn9numcftNp6Y7IJMFVJXXls2jsr6WotDuqQo0rqytqGXNmmpErBUnlUQEnw98+CAG/nxlv9OUeKOwc2GAnat9RBqUDRvc9yMcjhONxvH7d5/lFo8rsVicvDx3XFC/frnss08egwblM2BAHn375lBc7CZETeuz6Qw5k52SSXQiqrqjWZNnVk4B0WvEBX+Ow0pnHZ98sp7x4/dNd0Qmw83asJoFm9fvMfg4HI6xYEEltbVhgkGffSF2M40IGhHErxQeFaVgVJSdS/zUlwfQSNtze8Vi7qDoqqpGFi+uJBKJE4/rHklRLKaA0r9/HkOGFDJsWBHDh/dhwAA3KRowwO02s+4yk27JJDoLROSbgE9EDgKuAj5KbVgm1bQBioYIf570IY8ddYE7Y7sxnVBRvZ23ViwkP7h7DEhNTZgFCyqJRePWipNmGhU0CviUglERCg6LsnO5n/oVfuKNn31fdo8LcshtZx7ReFwJh2MsWlTJnDmb92ghisXigDBggJsIHXhgMYccUsL++xcxdGgRffqEUlJfY5pLJtH5GfBb3FPLnwBeB25OZVCmOwgScwgc1sDjTy3gsm8fme6ATAaqDTfw7MIy/M7uKx9v3lzH0qXbcRzZdSq06QFiQrxewFHyD4mQf3CUulU+6pb5idd1bu4sxxFyctyzvlrcZcydDmP+/K3MnLkRVfD5HKLROIWFQQ44oA8jR5Z6CVAh++9fRFGRJUCmayVz1lUdbqLz29SHY7qTRoVQnvDfWXP50ukHMHCgDUw2yYvF4/x30SwaohEKgzmoQvmqHVSsq7EBxz1Z3Et4RMkbESVvRIyGNT52LvMTqxaSnpMiCT6fkJvrJzfXD+xOYFSVSCTO0qXbmTt3CyD4fEI0GqdPnxAHHljMEUeUcuih/Rgxog/77VdoA6RNp7WZ6IjIpcDVwCHeokXAXar6SKoDM91Dw0KfQ5Xb7/uYP990WrrDMRlkSvliKqq3UxTMIRZVFi2uZPv2BkJBX8+dgNPsprsTnpz9YuTsHyVe79CwziG80Ud4mwOx1LyPIkIw6CMY9O3RhdWUAC1aVMnMmZu8CyoK8bgydGgRo0aVcsQRpYwYUczw4X2s+8skpdVEx0tyfg5cA8zCTfOPBm4XEVXVR9srXES+BPwV8AEPqeptrWz3VeAZYJyqlnW0EmYveNfWWRvawLRp6/jc5wanOyKTARZtWc/0ilUUhkI0NMRYsGArDfUxG4+TiVSIN7gPxafkHRgj74AoIIS3ODRUOES2+ojt7NrWnpa0lgDF40plZT2vvrqSl15agd/vdn8VF+dw6KEljBnTn4MO6svw4X3Yd98CfL7OdcWZ7NRWi86PgK+oannCsne8pORJoM1ER0R8wL3A6UAFMENEXlDVhc22K8RtNfqk4+GbrqCNQv4A5Y5/f8hjR3/VBiabNm3dWcNLS+eSGwhQXRVh4aJKVJVQls9Tlf0EjYHWu48RJdgvTnCfGBAlXi/d0trTEscR8vMD5OcHdi1TVcLhOLNnb+Kjj9YRCDiouusOOKCYkSP7MWpUKcOHu60/hYU2NUZv1VaiU9QsyQFAVctFpCiJso8FlqvqSgAReRI4D1jYbLubgT8A1yUVsUkBQaIOwUMaeeSJeXz/0jHpDsj0UA3RCE8vKAOFLRsbWLlyB36/2PiJbKRCPAxuK45+prUnUunQuNkhWuUQ3eF4rULdl/yIuLPCh0I++vbdvTwWi7NhQy0rVlQxefLyXYOfS0rc1p/Ro3e3/gweXGiXPegF2kp06ju5rslgYG3C8wpgfOIGInI0sJ+qviwirSY6InIFcAXA/vvvn8SuTUdpTAjmCC/Nm8dZGw5k0KCCdIdkepi4Ki8tmUtVfR2b1jawadNOuz5Or/HZ1h5/3zjB/jE0LiCgEQhXOkS2uIlPZIeDhrv/b8PncygoCFJQsLsFx239iTFr1iY+/HAdfr/bteU4wkEH9eXII/szcmQ/RowoZtiwIkKhZE5INpmirXfzMBH5tIXlAuz1lZFFxAHuAC5rb1tVfQB4AGDs2LG6t/s2LdNGoc/Byh//9hF3/O/pdqEvs4eP1ixj0eYNVCyrpbom2mtnHDeAChqG2K5ERsGBUP84oUEx95KyIsQbhUilO9YnWuUjUiUQ7/6/Gbf1x08o5P9M68+aNdUsXlwJgOM4xGJxhgwpZNSoUkaP7s8BBxQzYkSxDXzOYG0mOntZ9jpgv4TnQ7xlTQqBUcAU78NyIPCCiJxrA5LTRfCJsD5/Ix9PW8fxxw1Jd0Cmh1hWuYl3li9m1ZIaGhvi5IQcG3RsEgjEcbu6EpMfnxIaqIT2jeFetRDCWx0aN7gDnKPVApq+vyOfz6GwMLjH+J14XKmubuStt1bz6qur8Pvd5Gfw4AJOOGEIxx47kFGjSikpaedqiqbHaDXRUdXVe1n2DOAgERmOm+BcDHwzofwdQGnTcxGZAlxrSU56aVjI7Sf85akPOebor1oTrqGyrpan5sxgxZJqoo1KyC4CaJIiEIN4zHsMIEqgxO3yQqNoHMKbvcSn0kesNvVndrXHcYS8vAB5eXsOfK6uDvPUU4t5+uklxOPKoEH5TJgwmGOPHcQRR5RSWpqXxqhNW1L2LaaqURH5Ke6VlH3AP1R1gYjcBJSp6gup2rfZG4JEHEIHh/nXE/O44rKj0h2QSaOGaISHP/6A5cu2EwtjZ+SZveN1eWlC4hMa4HV3aQSNCo0bHcKbfIQ3+1qcoiIdRPY860vVnQvs2WeX8txzy4jHlQED8pkwYV/Gj9+XI4+0xKcnEdXMGvIyduxYLStLTaPP5fc8QcyJ4qh9mEtOnKoVyr0/+4oNTO6l4hrnnrffY+bKCuJ1gj9g1yYxKeYoTsJZ4A3rHepX+IlUOqS7pactqu5UFzU1kV3Pjz56AF//+iFMmDC41SkyTNtEZKaqjt3bcto9+iJyDvCyqtqM5b2INgiFw5Tb7v+Av0w8wwad9kIPvf4JM9dUEK/HkhzTPeK7L16IKDn7xskZHCZWJ9Qt9dFQ4W9z5vV0EdmzuyseV+bP38rs2ZsIBn2cffYBnHvuARx2WD/7LE2DZD69LgKWicgfReTQVAdkegrBQdhctIUPP65IdzCmm/1jchkfrlmONgp+v7VwmjRQId4gxOvBCSiFY6L0P6uBoqPD+IvjQM/tjXAcoaQkhwED8snPD/Dss0u59NJXOf/8yTzxxCK2bq1Ld4i9SruJjqp+CzgKWAFMEpGPReQK74rGJptFHEJFcNezH9LYGE13NKYbqCp/f3Qm765fiMQEn2MtOSbdBI2683LFGyFn/xglpzRScmojOftFEX/PTXjAHdc2cGA+AwbkUV3dyJ//XMaXv/wsP/3pW7z//lrC4Vi6Q8x6SX2KqWo17lxUTwKDgK8As0TkZymMzfQAEnXIOTDCP5+Ym+5QTIqpKn+5p4wpmxfg9ztI3JIc08MktPL48pSisRH6f7mBgtFhnLyePbpCRCgoCDJoUD79++cxa9Ymrr12Cqed9h/uumsWO3Y0pjvErNXuJ5mInCci/wWmAAHgWFU9ExgN/E9qwzNpFxf8AeGtlQupqKhOdzQmRaLROLfc+jFTNy0ir9iBiCU5picTNOK18kQhb0SU0i82kn94BAn07BYecLu2+vXLZcCAfHJz/Tz66ALOOec5nnxykbXwpEAyn2YXAHeq6hGqeruqbgZQ1TrgeymNzvQI2ijkD4Gb73uPTDtLz7QvHI7xm99M5aOK5fQd4RBvsMGSJoPEhXi9Q7wR8g+JUHpGAzlDIyCZ8VnV1LUVDPr405/KuOCCyUyZssY+a7tQMonORlV9P3GBiPwBQFXfTklUpocRfHFh56DtPPvi4nQHY7pQXV2En//8HWasXM2+xzYlOZbomAykbsKDQp+jI/Q7rZFg/yg9edByotxcP4MG5VNTE+Haa9/ju999jQULtqY7rKyQTKJzegvLzuzqQEzPpjGHQK7Dk5/MZNOm2nSHY7pATU2Yn/zkLeYu28jwL/ncCRjTeDl+Y7qCxoRYvYOTqxSfEKF4QhhfYc8ev5OoqCjIwIF5LFmyjUsvfZVf//p91q+3z9y90WqiIyI/EpF5wKEi8mnCbRXQ0mSfJts1CvlDlVvvf9+aVTPcjh2N/OhHb7Jo2VYOOs9BcL8gjMkWGnYHLQf7x+l3WiOFo8NIMDM+t0SE0tJcBgzI4+2313DBBZO5++5Z1NSE0x1aRmqrRedx4Bzgee++6XaMd8q56XUEJy5sK9nKq28tT3cwppO2bavniiveYNmKbRx8vg9frvaYS+0b07W8s7QaIHdElNIvNZB7YASczEh4HEcYMCCP4uIcHnlkAWef/RzPPLOESMQGLHdEW4mOqmo58BOgJuGGiJSkPjTTI8UcAjnCpPens21bfbqjMR20ZUsd3//+65SvruKgs/wE+sVt8LHJft74HY1B4RHuGVrBfTLn2mCBgMPAgfkEAg633Tadb3/7FdautbNgk9Veiw7ATKDMu5+Z8Nz0VmGH3P3i/N+DU9MdiemAjRt3cvnlr7F+fQ0HnBYgZ3CMeJ0NPja9SMw9JV0C7vidorGZ050F7oDlgQPzWL26mosvfonXXltlwwiS0Gqio6pne/fDVXWEd990G9F9IZqex+3C2pC3kXemrkp3MCYJ69bVcPnlr7FlSx1DJwTJOyBKzJIc00tpRIjXQc5+UUrPaCA0OHPOzmoav5Ob6+e3v53K//7vR9TVRdIdVo/W6qSeInJ0Wy9U1VldH47JGDGHQI7y4NsfM3b0vhQVhdIdkWnF6tU7+MEP3qCmJsx+RwcpHBUmVm9JjuntvNYdn1I8PkzjJh/VswPE6zLjYpm5uX5CoXxeemklc+du4Y9/PImDDuqb7rB6JGmt2UtE3m3jdaqqX0hNSG0bO3aslpWlpufs8nueIOZEcdQmMUyOEg/EGVq/LzddfWq6gzEtWLGiiiuueIP6+giDDgtQPCFCPAzYGVbGJFCcHEChZr6f+pX+jLrUwrZtDcRiyi9/OY6vfOWgrJkhXURmqurYvS2n1RYdVT1lbws32c7twlrprOejGWs5ftx+6Q7IJFiyZBs//OEbRCJxBhwQovi4RjSCJTnGfIZ7ZhaOUjQ6Qu7QGNVlQaLVmdG6U1KSQ2NjlFtvncbHH6/nhhuOs1b2BG1dR+cL3v0FLd26L0TTo8Uc/Dlw72sfUFdn13joKebP38L3v/86sZjSf78gfU9oRBU0akmOMa2KC7E6wV+olJzaSP5hEfBlxtidUMjPwIH5TJmylosuepH587ekO6Qeo6109STv/pwWbmenOC6TQSTs4BsQ5S+PfpzuUAwwe/YmfvjDNxGBkgFB+p4YRnzuBdSMMe0R4o1uC0/+oVFKT2sk0C8zrlvjOMLAgfnU1ka4/PLXefTRBcTjmZGopVJbXVc3evff7b5wTGYSfDFhYWwNs+at5+gj9k13QL3WtGnr+cUv3iUU8lHUN0DxhEacHLVr5RjTUepeWVlCSt+TGmlY46d2fiAj/peKi0OEwzH+8peZTJu2gZtvnkBJSW66w0qbdjsgRaSfiNwlIrNEZKaI/FVE+nVHcCaDxB18QfjrS1NpbMycC3Flk7ffXs1VV71NTo6fwj4B+owPE+gTd8ceGGM6RcNCvE7I2S9KvzMy58rKwaCPQYPymTFjA9/61iuUl+9Id0hpk8xIqyeBLcBXgQu9x0+lMiiTmZyIg/Zr5J4npqU7lF5n8uRl/OpX71NUFKKgwE/R0RFCA+J2GrkxXcK7snLEvbLy7pnRezYRYcCAfKqqGvjOd16hrGxjukNKi2QSnUGqerOqrvJutwADUh2YyUSCE3OYXbuK+Us3pzuYXkFVeeSRBdx888eUlOSQm+sj//AoOftHidWBJTnGdKG4e+0dJ8e9snKfzzXi5PX8mdH79cvFcYQf//gtXnyx981TmEyi84aIXCwijnf7OvB6qgMzmUniDr4A3DF5CuFwz//Fk8lUlfvum81f/jKT0tJcQiGH/EOjFBwaJW4tOcakTNPM6KGBcUq/2EjeIT3/7KzCwiB9+gS58caPuO++2b1qkHJbp5fXiEg18APcea/C3u1J4IruCc9kIicixIobueXhKTYPS4rEYnFuu+0THn54PgMG5BEMOhQcEaHg8AixejLqYmfGZCZvZvQwFBzuTRQ6sGdPJZGT42fAgDweemgev/nNVBoaeseP0bbmuipU1SLv3lFVv3dzVLWoO4M0GUYcJCqUO+uZ9LzNFNLVwuEYN9zwAU8/vZSBA/PwB4TCMRHyDoq6Y3IsyTGm+3jdWeJXio+LUHxCGF9Bz+3O8vsdBg3K5623VnPllW+ybVt9ukNKuaQu+ygifUXkWBH5fNMt1YGZzCbq4MQd3lm/kPdm2MSfXaWhIcq1107h9dfLGTQoH19AKDomQt6IqDsTuSU5xqSFRtzurGBpnH6nN9Lnc40ESmL0xBYe93o7eSxaVMl3vpP9Z2Qlc3r594H3ccfl/K93PzG1YZlsIHEHnwMPffwhK9duS3c4Ga+mJsxPfvIWH320jkGD8nH8UHxsmNz9YzYTuTE9gted5Y3f6XtSmJLTGt3Z0XvYKekiwj775LFtm3tG1syZ2XtGVjItOlcD44DV3vxXRwFVqQzKZA+J+ZCQcvMzb1Bdaxd06axt2+r5wQ9e59NPtzBwYD5OAIqPDxMcFLOzq4zpcXYnPL5cpc+xEfp/uYG8gyI4oZ6V8DSdkfWjH73Jyy+vSHc4KZFMotOgqg0AIhJS1cXAIakNy2QTX9QhnhfhhkmvE4v13L7rnmrjxp1cfvlrrFq1gwED8nCCUDyhkWD/mJ1dZUyPJl6XlqAKBaMilJ7ZQOFRYXyFPeez0D0jK8Tvf/8hf/vb7Kw7iSSZRKdCRIqBycCbIvI8sDqVQZlsI0hE2B7cwR8ffy/dwWSUZcu2c+mlr7Jx40722ScPXw70/XwjgZK4JTnGZJKYe9HBeCPkDo3R77RG+p7YSHCfnjGOJyfHzz775PHgg/P4/e8/JBzOjPm9ktFuoqOqX1HVKlWdCNwAPAycn0zhIvIlEVkiIstF5PoW1l8jIgtF5FMReVtEhnYwfpMhRBwk4rCofi1PvflpusPJCFOmrOHSS19h584w/fvn4eS4c+74Cy3JMSZj6e5urUBJnOIJYUq/3EDR0WFCg2M4OelLeprOyHrllZX87GdvU1MTTlssXSnZs66OFpGrgCOBClVtt/Yi4gPuBc4EDge+ISKHN9tsNjBWVY8EngH+2JHgTWZxcE87f2XFXKYvWJvucHosVeWhhz7lf/5nCrm5fvr2zcGXF6fk5EZ8uUq8wcGSHGMynTdLer2AQM7+MfqMC1N6ZgOlX2qgcEyY0KBYt4/pcRxh0KB8Zs3axPe+9xqbN+/s1v2nQjJnXf0e+BfQDygF/ikiv0ui7GOB5aq60kuMngTOS9xAVd9V1Trv6TRgSEeCN5nHUR8o3Pfe+6zdVJXucHqc+voI118/lfvum0P//nnk5QXwFcbpe3IjTkiJN1qCY0zWiXmtPF5Lj/iU3OEx+ox3W3v6nd5AwagwwQExJJD6xEdEGDgwn9Wra/jOd15l5cqqlO8zlZJp0bkEGKeqN6rqjcDngG8n8brBQOLP9gpvWWu+B7za0goRuUJEykSkbMuWLUns2vRkTtxH3Ilzy7NvsLM+O5pGu8KmTe6g47ffXs2gQfkEAg7B/lFKTm7ECWBJjjG9gqAxt6WnKfFxQkreQTGKjwvT/+wGSs9ooHhCIwWjwuQOixDcJ+ZepLCLp6HYZ59camrCXHrpq8yalbmnn/uT2GY9kAM0nRscAtZ1ZRAi8i1gLHBSS+tV9QHgAYCxY8emf9SW2Wu+qFAfaGDi46/zh++eheMk1YuatebP38LVV7/Lzp0RBg7MQxzIPyRC/mER96wNS3KM6aUEjYLumq1BEb8S6KcE93GfN10oVBwlHhZitUK0WojWOMR2OsTqmxInt7yOKCnJoaYmzJVXvsWtt57A6acP67KadZdWEx0RuRt3KPgOYIGIvOk9Px2YnkTZ64D9Ep4PoYUESUROA34LnKSqjcmHbjKaODgR2CTbuWvyh/z8ghPTHVHavPzyCm666WNCIR/9++fihJSicWFC+8RsSgdjTDOCxoBY07lazT4fHPAVKv5iRZwYGhcSN4vXC7GdQrRWiFULsQbHXVYnxBtp8fOmsDCI3+9w/fXvs2nTTi655HBEMudzqa0WnTLvfibw34TlU5IsewZwkIgMx01wLga+mbiBiBwF/B34kqpuTrJckyVEHCQMs7eW8/wHJZx3wsh0h9StYrE499wzm3/9awH9+uWQk+Mn0C9Gn8+FcQJqVzs2xnSQQBw0Dhrxnu9BwecmQYF+gOxuDcIdE02sQYhsF8KbHaJVPqI73K603Fw/Pl8ud9wxkw0bdnLNNWPx+TKjJb7VREdV/9X0WESCwMHe0yWq7iFsi6pGReSnuFNG+IB/qOoCEbkJKFPVF4DbgQLgaS87XKOq53a6NibjOOIQC8f478JZNMYifO3zozPql0Jn1dSE+e1vp/Lhh+sYMCAPv1/IOzhCwcgoGsU7s8oYY7qSuC1BsbYTodAAJTQo5vaXiRKrcWjc4hDZ6iPYJ4cnn1rM5s113HzzCeTkJDMCJr2kvSsgisjJuGddleMelf2AS1X1/RTH1qKxY8dqWVlZ+xt2wuX3PEHMibpnBpluFdMYEoRD+w7i2gtOJuDr+f88nbV2bTVXXfUOFRU17pWOQ9BnbJjQwBixBoF49id6xphMoYgfJMCu6xpqHHasi9NXC/jFDz7H4YMHpuQHqojMVNWxe1tOMj8b/wx8UVVPUtXPA2cAd+7tjo1J5BMfhIVF29Zz7aQX2Fpdm+6Qulw8rjz33FIuvvglNm7cycCB+QRLlH6nNRAc4E3MaUmOMaZHETSacBZYg6AR6LOvEBlcy51vvsOOup49j2EyiU5AVZc0PVHVpUAgdSGZ3soRByfisC2yk+uffJFPV65Pd0hdZtWqKi6//DVuvXUaubl+SktzyD0gQt+TGxE/xOvtIoDGmAyhgkYcnIiPcGOc2tqefZmQZPoHZorIQ8Bj3vNL2D1Q2ZguJeLgjyphiXLHW+/wlVFjOO/4UekOq9PC4RiTJs3n4Yfn7briqBOEomPC5OxrXVXGGJNqySQ6VwI/Aa7ynk8F7ktZRMaI4MNHrDHGcwtms3xjJVefdwJ+X2aNnZozZzMTJ35ERUUNpaU5BIIOoX1jFI6O4ITsrCpjjOkObSY63nxVc1X1UOCO7gnJGJfP8RFvjDN362p+9UgVN3ztdIoL8tIdVrtqasLcffcsnntuGbm5PgYNyiO4T4zCIxvxFcbdCwDW21lVxhjTHdr8tFXVGLBERPbvpniM2YMjDk7YYUu4ml8+/jyL1/bcyy2pKu++u4bzz5/Mf/+7jH32yaV0WJC+nw/Td0IEJ0+J17sD+4wxxnSPZLqu+uJeGXk6sGsaU7vejekuIg6+iFLvRPnDq29w0VFj+dK4Q9Md1h42b97J//3fJ7z/fgVFRUEGH5BLwagIoX1joEKsHqybyhhjul8yic4NKY/CmPaI4Fcf0XCMJ2bPYP6ajVxyylEMKumT1rC2bq3j9dfLue++OUQicfYdmkfB4VHyhruzmcTrbRyOMcakU1tzXeXgDkQ+EJgHPKy6e1oxY9LBLz5ijXE+3bqWec+spZ9TyJeOOJwvjD2g2wYr19dH+PDDdTz77DJmztxIPA4l/YOUjPSRf2gjOEq8weaoMsaYnqCtFp1/ARHcs6zOBA4Hru6OoIxpi89xIAoaj7PVX8O/53zC42UzGNl3CF898QhG7FfS5fuMxeLMmbOZF19cwRtvrCYajRMICIMOyCE0ME7BoVEkoO4s43EbaGyMMT1FW4nO4ap6BICIPExyM5Yb023EcfDFgTjEJca8mjXMe3kNuTvzOH3UoZz1+YPJzd27a1uuWlXFa6+t4rnnllFdHcYJKgMOC5I/xD1VXPxhEEXDdiaVMcb0RG0lOrsm7vQm6OyGcIzpHEd9bisPcRoL6nixfBbPzZ/FiNAgvvr5kYw5YiAAsZgSjcaJRuPEYkokEmthWZz587fwn/8sZeWqKnL7Q+nhPvYd7uAvUrwdEQ+DNtgYHGOM6cnaSnRGi0i191iAXO+5AKqqRSmPzpgOEtxuLUEJ5itrZAN3fLiBumcg2qjuzOBhIR6BeMSdnFebZvONya7HTq5ScqjDEac5iANI3Jvvxd2LMcaYzNBqoqNqU3ibTCZITHD/iOP0GYI7u64jTasRUXcy3ri3oInibhf3Wm3UWm2MMSZTJXN6uTEZzkEjbv6yJ0tejDEm29noSWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNZKaaIjIl8SkSUislxErm9hfUhEnvLWfyIiw1IZjzHGGGN6l5QlOiLiA+4FzgQOB74hIoc32+x7wHZVPRC4E/hDquIxxhhjTO/jT2HZxwLLVXUlgIg8CZwHLEzY5jxgovf4GeAeERFV1RTG1SYFlHi6dm+MMcZkDkl3AO1LZaIzGFib8LwCGN/aNqoaFZEdQD9ga+JGInIFcIX3tFZEliSsLm2+fWcVlO57mD/HH+qKsnqLxtpaCRUUpC0x7e3s+KefvQfpZcc/vRpqauSxH313nmosmoLih3ZFIalMdLqMqj4APNDSOhEpU9Wx3RyS8YhIWd32Sjv+aWLHP/3sPUgvO/7plQnfwakcjLwO2C/h+RBvWYvbiIgf6ANUpjAmY4wxxvQiqUx0ZgAHichwEQkCFwMvNNvmBeBS7/GFwDvpHJ9jjDHGmOySsq4rb8zNT4HXAR/wD1VdICI3AWWq+gLwMPCoiCwHtuEmQx3VYpeW6TZ2/NPLjn/62XuQXnb806vHH3+xBhRjjDHGZCu7MrIxxhhjspYlOsYYY4zJWhmT6Nh0EumVxPG/TES2iMgc7/b9dMSZrUTkHyKyWUTmt7JeROQu7/35VESO7u4Ys1kSx/9kEdmR8Pf/++6OMZuJyH4i8q6ILBSRBSJydQvb2P9AiiR5/Hvs/0BGXEcnYTqJ03EvPDhDRF5Q1cSrLO+aTkJELsadTuKi7o82+yR5/AGeUtWfdnuAvcMk4B7gkVbWnwkc5N3GA3/jsxfoNJ03ibaPP8BUVT27e8LpdaLA/6jqLBEpBGaKyJvNPoPsfyB1kjn+0EP/BzKlRWfXdBKqGgaappNIdB7wL+/xM8CpIpIBF6fOCMkcf5NCqvo+7pmJrTkPeERd04BiERnUPdFlvySOv0khVd2gqrO8xzXAItwr6yey/4EUSfL491iZkui0NJ1E84O8x3QSQNN0EmbvJXP8Ab7qNRk/IyL7tbDepE6y75FJneNEZK6IvCoiI9MdTLbyhiUcBXzSbJX9D3SDNo4/9ND/gUxJdEzP9yIwTFWPBN5kd+uaMb3BLGCoqo4G7gYmpzec7CQiBcCzwM9VtTrd8fQ27Rz/Hvs/kCmJjk0nkV7tHn9VrVTVRu/pQ8Ax3RSbcSXzP2JSRFWrVbXWe/wKEBCR0jSHlVVEJID7JftvVX2uhU3sfyCF2jv+Pfl/IFMSHZtOIr3aPf7N+sLPxe3DNd3nBeA73pknnwN2qOqGdAfVW4jIwKYxgSJyLO5nq/3Q6iLesX0YWKSqd7Symf0PpEgyx78n/w9kxFlX3TidhGlBksf/KhE5F3d0/jbgsrQFnIVE5AngZKBURCqAG4EAgKreD7wCfBlYDtQB301PpNkpieN/IfAjEYkC9cDF9kOrS00Avg3ME5E53rLfAPuD/Q90g2SOf4/9H7ApIIwxxhiTtTKl68oYY4wxpsMs0THGGGNM1rJExxhjjDFZyxIdY4wxxmQtS3SMMcYYk7Us0THGdJg3W/2+SWw3SUQu7ET5V4rId1pYPqxpBnERGSMiX05YN1FErk2ibBGRd0SkqKNxtVDWWyLSd2/LMcakjiU6xpjOuAxoN9HpLFW9X1XbmikcYAzudVM66svA3C6aQuBR4MddUI4xJkUs0TGml/NaSRaLyL9FZJE3KWuet+4YEXlPRGaKyOsiMshroRkL/FtE5ohIroj8XkRmiMh8EXmg6QqprexvHxGZ6T0eLSIqIvt7z1eISF5i64wXw1wRmQv8xFsWBG4CLvJiuMgr/nARmSIiK0XkqlZCuAR4PiGe73iT0c4VkUe9ZZNE5G8iMs0r62QR+Yd3fCYllPUC8I0OHnJjTDeyRMcYA3AIcJ+qHgZUAz/25ra5G7hQVY8B/gHcqqrPAGXAJao6RlXrgXtUdZyqjgJygbNb25GqbgZyvK6jE72yThSRocBmVa1r9pJ/Aj/zJgtsKiMM/B54yovhKW/VocAZwLHAjV4dmpsANCVaI4HfAV/wyr86Ybu+wHHAL3ATmjuBkcARIjLGi2M7EBKRfq3V1xiTXpboGGMA1qrqh97jx4ATcJOfUcCb3mXff4c7UWJLThGRT0RkHvAF3ISgLR/hJhyfB/6fd38iMDVxIxEpBopV9X1v0aPtlPuyqjaq6lZgMzCghW1KVLXGe/wF4Glve1R1W8J2L3qXsJ8HbFLVeaoaBxYAwxK220wKu/GMMXsnI+a6MsakXPO5YBQQYIGqHtfWC0UkB7gPGKuqa0VkIpDTzv7ex01shuJ2I/3K2+fLHQ99D40Jj2O0/BkXFRHHS1qSKSverNx4s3JzcOf2Mcb0QNaiY4wB2F9EmhKabwIfAEuA/k3LRSTgdfUA1ACF3uOmpGariBTgTu7XnqnAt4BlXsKxDXeQ8AeJG6lqFVAlIid4iy5JWJ0YQ0csAUZ4j98BvtbU9SQiJR0pyBuLNBAo70QcxphuYImOMQbcL/+fiMgi3LEpf/PGwVwI/MEbCDwHON7bfhJwv9el1Qg8CMzHneF+Rns7U9Vy3Bajpi6pD4Aqb8xLc98F7vX2lTjI+V3cwceJg5GT8TLuTOSo6gLgVuA9r453dKAcgGOAaaoa7eDrjDHdxGYvN6aXE5FhwEveQOKsJyKDgEdU9fQuKOuvwAuq+vbeR2aMSQVr0THG9CqqugF4sCsuGAjMtyTHmJ7NWnSMMcYYk7WsRccYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkLX+6A+goEbkMGJbmMIwxxhiTQqo6sSvKEVXtinKMMcYYY3oc67oyxhhjTNayRMcYY4wxWcsSHWOMMcZkLUt0jDHGGJO1LNExxhhjTNayRMcYY4wxWcsSHWOMMcZkrf8P/bbNq5a8WG0AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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U+Emq1yEihcA7cEoH27vO+1T1bOA0nKqw29vLg1uN+luc6rgyVS0B1vDW16M9u4ChIlLSwbau7ocx/YYFQMakQESKReRK4GngD6q6uodJ7QPKJKk7czvn+onbgNUnIkXArcAWVa3Bac/xDhH5L3d7mYhMS/qi/pGIFLlfcl/hre1dAFDVOM6X4L1JDXrHiMjlPbyurhThlBy0iMi5OCUynTkAxHHa5aTDp4BLVbUxeWWK9+1JnMbv13O0+guc+3eLWzokIlIgTmPvovYyICIni8ilbpuYFpwANN5VxsVpjH02MBs4BPyunX3OcfPhx2lv1ZKU9j6OvY8FOEHRAffYT+CUAHXJrVb7F/ArESkVEb+IJNprdet+GJNtFgAZ07nnRKQe59ftt3HG9PlE54d0TFU34LR/2eZWE7RXPZCPU81yGKfR8gk4PYBQ1Z04JRlfxakKWQFMdY/7As6X3zacEqoncRqlduQbwBZgoYjUAf8GTu7ptbkSvbYSj6+46z8L3OXey+/hBB0dcqtdfoTT7fuwiPSqHYmqblXV8g42d3rfVHWRu/04nC//xPpy4NM4VU+HcO7lTZ1kI4jT8Lwap4ppBPDNTvb/unu/anCq7pYCF7QN4lzFOAHIIZwqvBrgHnfbIzjtjg6LyGxVXYfTzulNnODoTOD1TvLR1sdwSs02APtxqvV6cj+MySpRTVtJszHGGGPMgGAlQMYYY4zJORYAGWOMMSbnWABkjDHGmJxjAZAxxhhjck6mJkTsc8OGDdPx48dnOxvGGGOMyaClS5dWq2q7g7Z2x6AJgGqGDuHgj76R7WxkXPzyD2c7C8YYY0zWiMiOrvfqmlWBGWOMMSbnWABkjDHGmJwzoAMgEblZRMpFpJzaumxnxxhjjDEDxIAOgFT1IVWdoaozGNLhxMfGGGOMMccY0AGQMcYYY0xPWABkjDHGmJxjAZAxxhhjcs6ADoCsEbQxxhhjemJAB0DWCNoYY4wxPTGgAyBjjDHGmJ6wAMgYY4wxOccCIGOMMcbkHAuAjDHGGJNzBnQAZL3AjDHGGNMTAzoAsl5gxhhjjOmJAR0AGWOMMcb0hC/bGUiXs4tLKb/8w9nOhjHGGGMGAFHVbOchLUSkHtiY7Xxk0TCgOtuZyJJcvnbI7evP5WuH3L7+XL52yO3rP1lVi3qbyKApAQI2quqMbGciW0SkPFevP5evHXL7+nP52iG3rz+Xrx1y+/pFpDwd6VgbIGOMMcbkHAuAjDHGGJNzBlMA9FC2M5BluXz9uXztkNvXn8vXDrl9/bl87ZDb15+Wax80jaCNMcYYY1I1mEqAjDHGGGNSYgGQMcYYY3LOgAiAROTdIrJRRLaIyB3tbA+KyDPu9kUiMj5p2zfd9RtF5PI+zXia9PT6RWS8iDSLyAr38WCfZ76XUrj2t4nIMhGJisg1bbbdKCKb3ceNfZfr9OjltceSXvc5fZfr9Enh+r8iIutEZJWI/EdETkjaNthf+86uPRde+1tEZLV7ja+JyGlJ23LhM7/d68+Fz/yk/T4kIioiM5LWde+1V9V+/QC8wFZgIhAAVgKntdnns8CD7vPrgGfc56e5+weBCW463mxfUx9e/3hgTbavIcPXPh6YAjwBXJO0fiiwzf1b6j4vzfY19cW1u9sasn0NfXD9lwD57vNbk973ufDat3vtOfTaFyc9vwp4wX2eK5/5HV3/oP/Md/crAuYDC4EZPX3tB0IJ0LnAFlXdpqqtwNPA+9vs837gcff5s8BlIiLu+qdVNayq24EtbnoDSW+uf6Dr8tpVtUJVVwHxNsdeDsxV1YOqegiYC7y7LzKdJr259sEglet/RVWb3MWFwFj3eS689h1d+2CQyvXXJS0WAInePDnxmd/J9Q90qXzfAfwA+AnQkrSu26/9QAiAxgC7kpYr3XXt7qOqUaAWKEvx2P6uN9cPMEFElovIqyJyUaYzm2a9ef0G+mvf2/yHRKRcRBaKyAfSmrO+0d3r/xTwrx4e29/05tohR157EfmciGwFfgp8sTvH9nO9uX4Y5J/5InIWcLyqPt/dY9saTFNhmLfaA4xT1RoRORuYLSKnt/n1YAanE1S1SkQmAi+LyGpV3ZrtTGWCiNwAzAAuznZe+loH154Tr72q/hL4pYh8FPgOMODaevVGB9c/qD/zRcQD/D/gpnSkNxBKgKqA45OWx7rr2t1HRHzAEKAmxWP7ux5fv1sUWAOgqktx6kQnZzzH6dOb12+gv/a9yr+qVrl/twHzgOnpzFwfSOn6ReQdwLeBq1Q13J1j+7HeXHvOvPZJngY+0MNj+6MeX38OfOYXAWcA80SkAjgPmOM2hO7+a5/tRk8pNIry4TRinMDRRlGnt9nncxzbCPhP7vPTObZR1DYGXoO43lz/8MT14jQqqwKGZvua0nntSfs+xlsbQW/HaQRb6j7PlWsvBYLu82HAZtppSNifHym+76fjfMCf1Gb9oH/tO7n2XHntT0p6/j6g3H2eK5/5HV1/znzmu/vP42gj6G6/9lm/4BRvynuATe5/+G+76+7C+eUDEAL+jNPoaTEwMenYb7vHbQSuyPa19OX1Ax8C1gIrgGXA+7J9LRm49nNw6nobcUr91iYd+0n3nmwBPpHta+mrawcuAFa7HwargU9l+1oydP3/Bva57+8VwJwceu3bvfYceu1/kfTZ9gpJX5I58pnf7vXnwmd+m33n4QZAPXntbSoMY4wxxuScgdAGyBhjjDEmrSwAMsYYY0zOsQDIGGOMMTnHAiBjjDHG5BwLgIwxxhiTcywAMsYYY0zOsQDIGGOMMTnHAiBjjDHG5BwLgIwxxhiTcywAMsYYY0zOsQDIGGOMMTnHAiBjjDHG5BwLgIwxxhiTcywAMsaknYg8KCLfTVNa40SkQUS87vI8EfnvdKTtpvcvEbkxXekZYwYGX7YzYIwZeESkAhgJRIEYsA54AnhIVeOqeks30vlvVf13R/uo6k6gsLd5ds93JzBJVW9ISv+KdKRtjBlYrATIGNNT71PVIuAE4G7gG8Aj6TyBiNiPNGNMRlgAZIzpFVWtVdU5wLXAjSJyhog8JiI/BBCRYSLyDxE5LCIHRWSBiHhE5PfAOOA5t4rr6yIyXkRURD4lIjuBl5PWJQdDJ4rIYhGpE5G/i8hQ91xvF5HK5PyJSIWIvENE3g18C7jWPd9Kd/uRKjU3X98RkR0isl9EnhCRIe62RD5uFJGdIlItIt/O7N01xmSKBUDGmLRQ1cVAJXBRm01fddcPx6k2+5azu34M2IlTklSoqj9NOuZi4FTg8g5O93Hgk8BonGq4+1LI3wvAj4Fn3PNNbWe3m9zHJcBEnKq3B9rscyFwMnAZ8D0RObWrcxtj+h8LgIwx6bQbGNpmXQQnUDlBVSOqukBVtYt07lTVRlVt7mD771V1jao2At8F/ivRSLqXrgf+n6puU9UG4JvAdW1Kn/5HVZtVdSWwEmgvkDLG9HMWABlj0mkMcLDNunuALcBLIrJNRO5IIZ1d3di+A/ADw1LOZceOc9NLTtuHU3KVsDfpeRNpaqBtjOlbFgAZY9JCRM7BCYBeS16vqvWq+lVVnQhcBXxFRC5LbO4gua5KiI5Pej4Op5SpGmgE8pPy5MWpeks13d04jbqT044C+7o4zhgzwFgAZIzpFREpFpErgaeBP6jq6jbbrxSRSSIiQC1Ot/m4u3kfTlub7rpBRE4TkXzgLuBZVY0Bm4CQiLxXRPzAd4Bg0nH7gPEi0tFn31PAbSIyQUQKOdpmKNqDPBpj+jELgIwxPfWciNTjVEd9G/h/wCfa2e8k4N9AA/Am8CtVfcXd9r/Ad9weYl/rxrl/DzyGUx0VAr4ITo804LPAw0AVTolQcq+wP7t/a0RkWTvpPuqmPR/YDrQAX+hGvowxA4R03RbRGGOMMWZwsRIgY4wxxuQcC4CMMcYYk3MsADLGGGNMzrEAyBhjjDE5Z9BMNDhs2DAdP358trNhjDHGmAxaunRptaoO73rPzg2aAGj8+PGUl5dnOxvGGGOMySAR2dH1Xl2zKjBjjDHG5BwLgIwxxhiTcywAMsYYY0zOGTRtgIwxxpjeikQiVFZW0tLSku2s5LxQKMTYsWPx+/0ZSd8CIGOMMcZVWVlJUVER48ePx5m/12SDqlJTU0NlZSUTJkzIyDmsCswYY4xxtbS0UFZWZsFPlokIZWVlGS2JswDIGGOMSWLBT/+Q6dfBAiBjjDHG5BxrA2SMMcZ04OqrZ1NRUZe29MaPL+avf/1Au9tqamq47LLLANi7dy9er5fhw50BjxcvXkwgEOgw3fLycp544gnuu+++HuXrscceo7y8nAceeKDDfebNm0cgEOCCCy7o0Tnac/jwYZ588kk++9nPpi3NVFkAZIwxxnSgoqKOMWMK05peR8rKylixYgUAd955J4WFhXzta187sj0ajeLztf+1PWPGDGbMmJG2fLZn3rx5FBYWpj0A+tWvfpWVAMiqwIwxx1BVVqzYz89/Xs7q1QdQ1WxnyZicddNNN3HLLbcwc+ZMvv71r7N48WLOP/98pk+fzgUXXMDGjRsBJzi58sorASd4+uQnP8nb3/52Jk6c2GGp0O9+9zsmT57Mueeey+uvv35k/XPPPcfMmTOZPn0673jHO9i3bx8VFRU8+OCD3HvvvUybNo0FCxa0ux/Aq6++yrRp05g2bRrTp0+nvr4egHvuuYdzzjmHKVOm8P3vfx+AO+64g61btzJt2jRuv/32jN3H9lgJkDEGgFgszmuvVfHQQ6vYtOkgsZjyxz+uZ+rUEdxyy1TOPnukNQ41JgsqKyt544038Hq91NXVsWDBAnw+H//+97/51re+xV/+8pe3HLNhwwZeeeUV6uvrOfnkk7n11luPGU9nz549fP/732fp0qUMGTKESy65hOnTpwNw4YUXsnDhQkSEhx9+mJ/+9Kf87Gc/45ZbbjmmVOrQoUPt7vd///d//PKXv2TWrFk0NDQQCoV46aWX2Lx5M4sXL0ZVueqqq5g/fz533303a9asOVLy1ZcsADImx7W0RHnhhe389rer2L+/iVDIy8iR+YgIqsq6ddXceutcJk8eyq23TmXWrDEWCBnThz784Q/j9XoBqK2t5cYbb2Tz5s2ICJFIpN1j3vve9xIMBgkGg4wYMYJ9+/YxduzYI9sXLVrE29/+9iNtjK699lo2bdoEOAHXtddey549e2htbe1wHJ6O9ps1axZf+cpXuP7667n66qsZO3YsL730Ei+99NKRIKuhoYHNmzczbty49NykHrAqMGNy1OHDLfzud6t597uf5Uc/WkhTU4RRowooKQkdCXCcsTjyGDkynx07avnyl1/hmmvm8J//7CAajWf5CozJDQUFBUeef/e73+WSSy5hzZo1PPfccx2OkxMMBo8893q9RKPRlM/3hS98gc9//vOsXr2a3/zmNx2eo6P97rjjDh5++GGam5uZNWsWGzZsQFX55je/yYoVK1ixYgVbtmzhU5/6VMp5ygQLgIzJMVVV9fzf/y3hiiv+wq9+tQK/38OoUQUUFnbcw0REKC0NMWpUPtXVTdxxx3w+8IG/8fzz22htjfVh7o3JbbW1tYwZMwZwem711MyZM3n11VepqakhEonw5z//ud1zPP7440fWFxUVHWnP09l+W7du5cwzz+Qb3/gG55xzDhs2bODyyy/n0UcfpaGhAYCqqir279//ljT7klWBGZMjNm8+xKOPrubf/94BQFlZHn5/934DiQjFxUGKi4M0NLRy552v84tfLOUzn5nKe987kVDIPlLM4DJ+fHHau8H3xte//nVuvPFGfvjDH/Le9763x+mMHj2aO++8k/PPP5+SkhKmTZt2ZNudd97Jhz/8YUpLS7n00kvZvn07AO973/u45ppr+Pvf/87999/f4X4///nPeeWVV/B4PJx++ulcccUVBINB1q9fz/nnnw9AYWEhf/jDHzjxxBOZNWsWZ5xxBldccQX33HNPz29ON8lg6eExY8YMLS8vz3Y2jOmXli/fx+c+92+iUWXYsBBeb/oKfxsbI8QKWznhbT4++4GZnDNxPF6PFS6bgWn9+vWceuqp2c6GcbX3eojIUlXtdZ9/+7lmzCC3efMhvvCF/xAIeCkr67iaq6dKxwslszxEIlEefX0hi/Zv5ZKJp3DaiOPwebxpP58xxqSDBUDGDGKVlfXccstcAIqK0h/8BEZGKTk/gkbBG/VQXx1jo9TQFF3FvIoNXDhuMmeOHIvfa4GQMaZ/sXJqYwap6uombrnlJZqaIpSWhtKefmBUlJILnOBHowIihIIeDh1o5cCuMHFVXtq6hgcW/YfFVdsIR9vvrmuMMdnQZQAkIj8TkdP7IjPGmPSor2/lc5/7DwcONDNsWF7a0w+Odkt+Im7wkyBCMOhlz94G9u1upjiYh0eEl7et54HFL/PGzi00R1rTnh9jjOmuVEqA1gMPicgiEblFRIZkOlPGmJ5raYly220vs23bYUaMyE97+sHjogw5L4K2tgl+XCJCIOCloqKWAwea8Hu9FAfz8ImH+Ts28svFLzO/YiNNFggZY7KoywBIVR9W1VnAx4HxwCoReVJELunsOBF5VET2i8iaDraLiNwnIltEZJWInJW07UYR2ew+buzeJRmTu6LROHfcMZ8VKw4wcmQGgp8xUUpmusFPrOPRoD0ewe/3snHjIWprwwBHAqGA18sbu7bw+5VvEInZGELGmOxIqRG0iHiBU9xHNbAS+IqIfEZVr+vgsMeAB4AnOth+BXCS+5gJ/BqYKSJDge8DMwAFlorIHFU9lNIVGZOjVJUf/vBN5s+v5LjjCtI+XUVwbJQh57QSaxXoJPhJ8HoFVWHt2hqmTRtBfr7zcePzOIHQoeZGXtu5mUsmnJLWfBqTTr9ZMo+DzQ1pS29oXiGfOeft7W6rqanhsssuA2Dv3r14vd4jU1UsXryYQKDzjgzz5s0jEAi0O1v7Y489xu23387YsWNpaGhg4sSJfP/73+9yZvfZs2czefJkTjvttBSuLjUVFRW88cYbfPSjH01bmj3RZQAkIvcCVwIvAz9W1cXupp+IyMaOjlPV+SIyvpOk3w88oc5ARAtFpERERgNvB+aq6kH3/HOBdwNPpXA9xuQkVeW++5YxZ85WRo9Of/ATGheleEYr8XBqwU+Cz+ehtTXG6tUHmD59BIHA0d5gBf4giyq3cnLZSI4rLk1rfo1Jl4PNDQwJpa80tbNgqqys7MikoHfeeecxE4+mYt68eRQWFnYY1Fx77bU88MADALzyyitcffXVvPLKK52OezR79myuvPLKtAdATz75ZNYDoFTaAK0CpqnqZ5KCn4Rze3HuMcCupOVKd11H699CRG4WkXIRKT9w4EAvsmLMwPb736/j8cfXMnJkPh5PmoOfE9zgp6V7wU9CIOAlGomzZk01sdjRgVe9Hg8+j5c5G1dYVZgxHVi6dCkXX3wxZ599Npdffjl79uwB4L777uO0005jypQpXHfddVRUVPDggw9y7733Mm3aNBYsWNBpupdccgk333wzDz30EAC//e1vOeecc5g6dSof+tCHaGpq4o033mDOnDncfvvtTJs2ja1bt7a7H8Cf//xnzjjjDKZOncrb3vY2AGKxGLfffjvnnHMOU6ZM4Te/+Q3gzBW2YMECpk2bxr333pupW9elVAKgG1S1MXmFiPwHQFVrM5KrFKnqQ6o6Q1VnJIoJjck1zz23hV/8YinDh+fh86V3ZIvQhAhDznaDn3jPA6tA0EtTY5T162tIHn0+3x/gcEsTC3ZsSkd2jRlUVJUvfOELPPvssyxdupRPfvKTfPvb3wbg7rvvZvny5axatYoHH3yQ8ePHc8stt3DbbbexYsUKLrrooi7TP+uss9iwYQMAV199NUuWLGHlypWceuqpPPLII1xwwQVcddVV3HPPPaxYsYITTzyx3f0A7rrrLl588UVWrlzJnDlzAHjkkUcYMmQIS5YsYcmSJfz2t79l+/bt3H333Vx00UWsWLGC2267LUN3r2sdVoGJSAjIB4aJSCmQ+PQrpoMSmW6qAo5PWh7rrqvCqQZLXj8vDeczZtBZsGAX//M/bzJ0aOiY6qV0yJsYoXhahFgvg5+EYNDDoUMtbNlymJNOOlrlVRgIsrhqG5OHjWKsVYUZc0Q4HGbNmjW8853vBJwSldGjRwMwZcoUrr/+ej7wgQ/wgQ98oEfpJ/8YWbNmDd/5znc4fPgwDQ0NXH755e0e09F+s2bN4qabbuK//uu/uPrqqwF46aWXWLVqFc8++yzgTJ66efPmLtsy9ZXO2gB9BvgycBywLGl9HU7j5t6aA3xeRJ7GaQRdq6p7RORF4Mdu0AXwLuCbaTifMYPK8uX7+NrXXmXIkEDaJyH1D4tRNDV9wQ9wZIygvXsbCQZ9jBtXBIBHPPg9XuZsWM5/n/02Al4boN4YcAKU008/nTfffPMt255//nnmz5/Pc889x49+9CNWr17d7fSXL19+pP3PTTfdxOzZs5k6dSqPPfYY8+bNa/eYjvZ78MEHWbRoEc8//zxnn302S5cuRVW5//773xJMdZR2X+uwvFxVf6GqE4CvqeqEpMdUVe0yABKRp4A3gZNFpFJEPuWOI3SLu8s/gW3AFuC3wGfd8x4EfgAscR93JRpEG2McVVX13HbbK4RCPvLz/WlNW3zKkHNanTF+0hX8JNJ2xwjasaOW/fubjqzP8weoCzczv6LDfhXG5JxgMMiBAweOBECRSIS1a9cSj8fZtWsXl1xyCT/5yU+ora2loaGBoqIi6uvrU0r71Vdf5aGHHuLTn/40APX19YwePZpIJMIf//jHI/u1TbOj/bZu3crMmTO56667GD58OLt27eLyyy/n17/+NZGIMwr8pk2baGxs7FY+M6mzKrBLVfVloEpErm67XVX/2lnCqvqRLrYr8LkOtj0KPNrZ8cbkqubmCF/5yjzC4VhGRnkuOjOCN6TEmjMzU05ijKBNmw6Rl+c7MkdZYSBI+e4KTh42muOHDM3IuY3prqF5hWnvBp8qj8fDs88+yxe/+EVqa2uJRqN8+ctfZvLkydxwww3U1taiqnzxi1+kpKSE973vfVxzzTX8/e9/5/77739LO6BnnnmG1157jaamJiZMmMBf/vKXIyVAP/jBD5g5cybDhw9n5syZRwKU6667jk9/+tPcd999PPvssx3ud/vtt7N582ZUlcsuu4ypU6cyZcoUKioqOOuss1BVhg8fzuzZs5kyZQper5epU6dy0003Za0dkCTXAR6zQeR/VPX7IvK7djarqn4ys1nrnhkzZmh5eXm2s2FMRqkq3/nOa7z4YgWjRuWnvbt7YGSU0lmtxJqEo83+MiMSieP1yjHd45sjrYT8AT5tVWEmS9avX99pt3DTt9p7PURkqarO6G3aHX7CqOr33b+f6O1JjDHp8dRT63nhhe2MGpX+sX4koBTPiBBvzXzwA+D3ewiHY6xfX8OUM4cjHjlSFTZv+wbeNemMjOfBGJO7UpkM9UsiUuxOXfGwiCwTkXf1ReaMMUeVl+/l3nuXMmxYXtrH+gGlaEoET0Dbnd8rU4IBD3V1rWzddnREjcJAkGV7drDjcHWf5cMYk3tSqeT/pKrW4fTGKgM+Btyd0VwZY46xZ08DX/vaqxQU+NPe3R0gODpG3rgY8ea+C36Ao7PH72lg3z6nUXSiV9hzG1cSjkb6Nj/GcGz3cJM9mX4dUgmAEp+I78GZumItfVE+bowBErO7v0JLS+RIg+F08gQTVV+Qjf/aIk6j6M2bD1Ff78wQn+cP0NDawivbN/R5fkxuC4VC1NTUWBCUZapKTU0NoVAoY+dIpZXhUhF5CZgAfFNEioB4xnJkjDlCVfnRjxayZcthRo1K/+zuoBRPb0W8SrwlM72+UuH1CvG4sG5dzZFG0YWBECv27uSUYaMYX2ojvZu+MXbsWCorK7HplbIvFAoxduzYjKWfSgD0KWAasE1Vm0SkDLCG0cb0gWee2cDzz2/LyASnAKHjYwSOixFvyn6hbttG0R6PEPD6eG7jSj4942JCvvSOd2RMe/x+PxMmTMh2Nkwf6PInn6rGgX3AaSLyNuB0oCTD+TIm5y1dupef/cyZ4yv9jZ7Bk6cUTY+g4b7p9ZWKto2iQz4/jZEwL29bn+WcGWMGmy5LgETkJ8C1wDogMWWzAvMzmC9jclqi0XN+vi8jjZ5BKT67FfHgdnvvJ5IaRRcVBRg5Mp/CQIhV+3ZxctkoTiwbke0cGmMGiVSqwD4AnKyq4QznxRiD0+j5q1+dR3NzlOHD0z/SM0DohCjBETF3wMP+JblRdH6+M1J00Otj9oZlfHzaLIYXFGU7i8aYQSCVVo/bAKt8N6YPqCr/+7+L2LTpEMOGZab3gzc/TvG0KLF+VPXVltcreDxOo+jW1hhBnx9FeXr1IurDzdnOnjFmEEilBKgJWCEi/wGOlAKp6hczlitjctSzz27iH//YmpGRngEQp8s7AsT6Z/CT0LZRdL4/SEM4zDNrlvCxqecT7CeNolWVlmiEhtYwDa0tNLSGqQs3c6i5kcMtTdSGWwh4vFx75rkUBzNTomeM6b5UAqA57sMYk0HLl+/jpz9dnKGRnh15E6MEhvXPqq/2OI2iI2zdVsukSSUUBoNUN9Xzt/XL+PDp5+D19H3X/eZIK6/t3My+hlpqwy00hFtQFI84eVFV4hrH6/Hi83jwioeGcAtPr17Ex6ZeQJ4//WM5GWO6r8sASFUfF5E8YJyqbuyDPBmTc6qq6rnttnnk52dmpGcAb2GcojOjxFr6b9XXW4gQDHrYs6eBwkI/o0YVUBQIsf1wNS9uWcMVJ52ZmZKyDuypP8xf1i2lobWFgNeHVzwUBIJ4usiD3+vlUHMTz64t57ozZ+L3ZuY1NsakLpW5wN4HrABecJeniYiVCBmTJg0NrXzpSy/T0hKluDgzpQPegjgl57c6/TfjAyT4cYkIgYCXzZsPU13djIhQFAixYu8u3ty1tU/yoKqUV23niRVvEI5GKA7mEfL58Xu9XQY/CYWBIJX1h5izcTmxuI0la0y2pVJ+fCdwLnAYQFVXABMzliNjckg0Gufb336NHTvqMtTjSwmOjVJ2WRhvgRIfoH05PR7B7/ewYcNBDh1qwSNCUSDIqxUbWbu/KqPnbolG+Nv6Zczduo48v7/HVVgiQnEgxKbqfczdutamWjAmy1IJgCKqWttmnf18MSYNHnhgOa+9VsnIkemf5kJ8ToPnknMjaAziA6nqqx1er+DzOj3D6urCeD0e8v0B/rFxZcZmjt/XUMujyxawqWYfxcEQPk/vqq5EhKJgiOV7dvDGri1pyqUxpidSCYDWishHAa+InCQi9wNvZDhfxgx6zz23hd//fi0jRuSnvR2LryRO2TvChI6PEmsC7ec9vlLl9XnwiLBmTQ2NjRH8Xi9+r5dn15ZT3ViftvOoKsv37ODxFa/TFGmlOBhK22vkEaEwEGJ+xSZW7tmZljSNMd2XSgD0BZzpL8LAU0Ad8OVUEheRd4vIRhHZIiJ3tLP9XhFZ4T42icjhpG2xpG3W5sgMKsuX7+MHP1jI0KEhfL509mRS8k6KMPSSMBJQ4s0eBnKpT3t8fud+rVp1gObmKCGfn7gqT61ZTH24pdfph6MR5mxczgub1xD0+snPQK8tr8dDQSDAv7asZkvNvrSnb4zpmmSqHlpEvMAm4J1AJbAE+Iiqrutg/y8A01X1k+5yg6oWpnq+GTNmaHl5ee8zbkyGVVXVc/31/0RV09ro2RNSime0OiM8t8iAa+zcXa2tMXw+D9OmjSAY9NIQbqEsv5AbejFG0P7GOv66bimHW5ooCqSv1KcjrbEorbEY1085jzHFpRk9lzGDhYgsVdUZvU2n05+eInKjiCwTkUb3US4iH08x7XOBLaq6TVVbgaeB93ey/0dwSpiMGbQy1eMrMDJK2TtbCAyLO2P8DPLgByAQ8BKNxlm16gCRSJyCQJADTfXM3tD9Xlaqyqq9u3hs+evUh1soDub1Sff6gNeHz+PhmTWLqWlqyPj5jDFHdTgOkIjciFPV9RVgGU45+lnAPSKiqvr7LtIeA+xKWq4EZnZwrhOACcDLSatDIlIORIG7VXV2O8fdDNwMMG7cuC6yY0x2xWJHe3yNGlWQnkQ9SuEZEQomRYm3itvQOXcEAl7C4RirVx9gypThFAVCbDt4gJe2ruHySWfQGos5pSzRKOFYlNaY+zcaJRyN0BSN0Bxp5WBzIxWHqynwB/t8jJ6Qz09ja5gnVy3kpumzKLLRoo3pE50NhHgr8EFVrUha97KIfAinNKerAKg7rgOeVdVY0roTVLVKRCa6512tqscM+qGqDwEPgVMFlsb8GJN2iR5fo0f3JvhRxA+egOLJU4qnRfAWKbFmAc2t4CchGPTS1Bhl7ZpqzjhzOEXBECv27GLFnp2ICB4RxG0HpYCi7mjNemSb1yMUB/NSHtMn3QoCQRpaW3h69WI+Nu0CQv1kmg9jBrPOAqDiNsEPAKpaISLFKaRdBRyftDzWXdee64DPtTlPlft3m4jMA6YDfTPqmTFp9txzW3j88bWMHNlxjy9vfhxvkeIJKBJQvHnOwxNy2vd4gk7wAzjf5O7feHNuBj7JgkEPdfUR1q+r4fTTyygOOhPJ9uUo0b1VGAhR09zAn9cu4bozbLRoYzKtswCosymXU5mOeQlwkohMwAl8rgM+2nYnETkFKAXeTFpXCjSpalhEhgGzgJ+mcE5j+p2VK/fzgx8spKysbY8vxVukBEfFyBsfw1uobmCjiAdUBeKgcUCdv9oMg61XV1qIEAp6OHS4hQ0bD3LKKWUMoNjniKJAiMq6Q/xz8yquOnnagArgjBloOguAThWRVe2sF1IYCVpVoyLyeeBFwAs8qqprReQuoFxVE13brwOe1mO7o50K/EZE4jgNte/uqPeYMf3Z7t0NfPnLr5CX5yUU8gGKr0QJjo6Rd0IMTygOAhoR4keCG/vS6xERgkEv1dXNbN58iMmTB16vqsRo0esO7ObUYaOZPGxUtrNkzKDVYTd4t2Fyh1R1R0Zy1EPWDd70N42Nrdx44wtU7a5j9ElBgmNihI6P4QkAomhE0ChYwJNeqko4HGPkyAJOmlSCeAbe/Q1HI4gIn5nxdps93pg20tUNvsMSoP4W4BgzkITDUb76nf8QHlvLqe/yIN5WEIi3DvwpKfo7cUuC9u1rpKUlymmnlaV5sMnMC/r81IVb+M+29Vx58tRsZ8eYQWlgfSoYMwC0RqJ84xcv0ji5mhFTPEfm4Yo3CwySKSn6OxEhFPRSV9fK8uX7aWmJZjtL3VYYCLJ6fyXbDh7IdlaMGZQsADImjQ42NfDNJ5/jYEkNXnWDnhwYlLBfckuCWltjLF+2n7q6cLZz1C0eEYJeH89vWkk4Gsl2dowZdLoMgETkfSJigZIxnYhrnCWV2/jhP19gf1M9nlYPovbfpj8IBJzu5KtWVbN/f1OWc9M9IZ+fxkiYeRUbs50VYwadVD6hrwU2i8hP3S7rxpgkh5ob+ePKhfypfBl7K5vwRr3Yb4b+xef34PN52LjxIDt21GU7O91SGAiyfM8OdtUezHZWjBlUuvyUVtUbODoI4WMi8qaI3CwiRRnPnTH9WFyVZbt38PCy+azdtY/KrU34vd4B2esoF3i9QjDgZefOOjZsOEg8PjAGj/eIB7/Hy3MbVxCJxbo+wBiTkpR+pqpqHfAszhQYo4EPAsvcGdyNyTmHm5t4atVCXtyyhsbaKDs2NRAIePFY8NOvicdpF3TgQPORSVQHgjx/gNqWZl7buSnbWTFm0EilDdD7ReRvwDzAD5yrqlcAU4GvZjZ7xvQvqsqKPTv57dJXqao/hDYLmzYcxu8XC34GCHFHjW5oiLB82T6amgZGD7HCQJDFldvZU1+b7awYMyikUgJ0NXCvqp6pqveo6n4AVW0CPpXR3BnTjxxqbuTpNYv41+bVBLw+aPWwdl0NXq/g9VqbnwHF7SEWicZZsWIfhw/3/x5iXo8HjwjPbVxONG5VYcb0Viqf2ntVdX7yChH5CYCq/icjuTKmH2mNRZlfsZHfLn2VXbUHKQ6GiIaV1aurEZEBN8ieOSoQ8CIirF5dzc6ddWg/bxdUEAhysLmRhZXbsp0VYwa8VD6539nOuivSnRFj+htVZVP1Xn6zZB5v7NpCni9AYSBEa2ucVasPEI8rfr8FPwOdz+chEPCwY0cdy5btp6Ghf4+5U+AP8vrOzRxorM92VowZ0Dr89BaRW0VkNXCKiKxKemwH2psk1ZhBo6apgadWL+Iv65YSjccoDubh9XiIROKsWnWAaDR+ZHwZM/B5PEIo5KMlHGXF8v1UVNT2215iXo8HQfjHppXE4gOjEbcx/VFns8E/CfwL+F/gjqT19apqA1KYQSkcjfDGri0srtzuzMwdDCHiNG6ORuOsXn2AcDhGMGjBz2AUCHjRuLJrVwMHDjRzyslDKSruf5ORFvgD7K2vZemeCs4dMzHb2TFmQOosAFJVrRCRz7XdICJDLQgyg4mqsu7AHuZuXUNLNEKBP4jXc7SAtKUlypo11bS0xAgGrNprMBOPEAp5iURirFx5gOPGFHDCCUPwevtPLz8RoSAQYN72jUwaOpKheQXZzpIxA05XJUBXAksB5djpqxWwnx1mUNjfUMcLW1ZTVXeYkM9PcTDvmO31da2sWVtNPK5W8pND/H4v6lWqqhqprm7m5JOHMmRIMNvZOsLn8aJEeH7jSq6fej4e6T8BmjEDQYcBkKpe6f6d0HfZMabv1IebeXPXVpbv2YkIx1R3JRw40MTGjYfwesTa/OSgo6VBcVatqmb06AImjB+C19c/go1Cf5DK+kOUV23n3LH2m9SY7ugwABKRszo7UFWXpT87xmReTVMDiyq3sWZ/JXFVCgNBPO3M3bVzZx07dtTh93v7VfWH6Xt+vwefT9m7p5GammYmTy6ltDSU7Ww5VWH+AP/Ztp5YPM55x5/4liDeGNO+zqrAftbJNgUuTXNejMmoPfW1vLlrC5tq9iI4bSjaC3w0rmzafIj9+5tsegtzhIgQDHmJRuOsWVPN0KEhJk4oIS+/s4/RzPN5vBQGgrxSsZGmaCuXTjjVgiBjUtBZFdglvU1cRN4N/ALwAg+r6t1ttt8E3ANUuaseUNWH3W03At9x1/9QVR/vbX5M7lFVdtTW8PqOzeyqO4iIUBgIddheIhqJs259DbW1YYJBr32RmLfw+Tx4vcKhQ2GWHtrHqFEFnHBCcVbHhPJ6PBQHgyyq3EZzJMIVJ515TCN+Y8xbdVYFdqmqviwiV7e3XVX/2lnCIuIFfokzkGIlsERE5qjquja7PqOqn29z7FDg+8AMnNKmpe6xh7q8ImOAuMbZXLOP13Zs5kBTPV7xUBR4axufZM3NTk+vcDhGKOgFC35MB8SdSkPjTrXYvn2NjBtXzJgxhVkrMfSIh+JgHqv3VdISjfD+U6bj91q7NWM60lnZ7cXAy8D72tmmQKcBEHAusEVVtwGIyNPA+4G2AVB7LgfmJrrai8hc4N3AUykca3JYJBZj/YHdLNi5mfpwM36Pt8vAB6CuLszaNTXE1Xp6mdSJx6kWi8eVHRV1VFU1MHHiEEYMzz+232wf8bhjV20+uI8/rVnMh06fQcjn7/uMGDMAdFYF9n337yd6mPYYYFfSciUws539PiQibwM2Abep6q4Ojh3T9kARuRm4GWDcuHE9zKYZDFSVLQf388Lm1TRGwgS9vrd0Z+/I/v1NbNpkPb1Mz3ncQCgWjbNx4yEqd9Vz4qSSrHSbFxGKAyF21R3kyVVvcu0ZMykI9J/u+8b0F11WEotImYjcJyLLRGSpiPxCRMrSdP7ngPGqOgWYC3SrnY+qPqSqM1R1xvDhw9OUJTPQ1Idb+Ov6ZTy7rpyIO21FMIVfvaqwY0cdGzcexOfz4LN5vUwveX0eQkEPLeEoq1ZVs2ZNNU1N0T7Ph4hQFAhR3dTIEyveoLalqc/zYEx/l8on/tPAAeBDwDXu82dSOK4KOD5peSxHGzsDoKo1qhp2Fx8Gzk71WGPiqqzYs5PflM9jc80+igOhlIv7a2vDLFu6j5076wkGrJu7SSMR/H4voaCHw4fDLFu2jy2bDxGJ9O28XU6D/yANrS08seINqpts8lRjkolq5xP+icgaVT2jzbrVqnpmF8f5cKq1LsMJXpYAH1XVtUn7jFbVPe7zDwLfUNXz3EbQS4HEWETLgLM7m35jxowZWl5e3um1mMGjuqmef25aRVXdYfL9gZQbe7a0RNm2rZaamha8XrHZ3E3GqSqt4TgerzB+fDGjRxf0ee/CxtYwXo+H686cyXFFJX16bmPSTUSWquqM3qaTygAWL4nIdcCf3OVrgBe7OkhVoyLyeXdfL/Coqq4VkbuAclWdA3xRRK4CosBB4Cb32IMi8gOcoAngLpt7zIDTyHlR5VZe37UFQdodvbk9sZiya1cdlZUNAASDHuvibvpEYvygWEzZurWW3bsbmDSplJKSvmuXUxAI0hxp5clVC7nmtLMZX2pNBozpsARIROo5OgdYAZAov/UADapa3Cc5TJGVAA1+u2oP8vymlRxuaXrLZKWd2b+/iW3baolE4gQCHhvY0GRVJBInFlPKykJMnDiEUKjvBlIMRyO0xmKcPmIMIwqLKQnlMyQYYkgon6DXZz8KzICQ8RIgVS3qbeLGpENzpJV5FRtYsXcXfo835d5d9fWtbNlymIaGVvw+D6GQ9fAy2ZeYVuPgwRYOHWxh7PFFjB1b1Cft0II+P16Ph3UHqli9rxKvRxCEmMbxe72UhgoYll/I8IIiSvMKKA7mMSSYh0eEaDxOLB4nGo8R1ThR97mzzt2mMVRhYukwilL8f2pMtqT000NESoGTgCOT36jq/ExlypiEzTX7eH7TSsLRCEWdjOCcrLU1xvbttezf34zHgw1qaPqdxECK8biyc2c9e/Y0MmlSCcOGZT5ocKbOOPbHgKoSV6W2pYnqpgbW7t+NxyN4EOKqKIog7n8jOTLE0dH6AyVRmaAoHhHOGDGWmWMnUpZfmPFrMqYnugyAROS/gS/h9MRaAZwHvInNBWYybOXeXfxz8ypCXn9KvyYjkTh79zayc2cdqtbOx/R/Hne2+Wg0zvr1Byku9jNpUikFBX07eKGI4BXB6/GQjpZJcY2zel8lq/dVMmnoCC4YN4nR1vja9DOplAB9CTgHWKiql4jIKcCPM5stk+vKq7Yzd+s6CgIBfJ7Oq65qa8Ps3t1ATU0Lqlg7HzPg+HwefF6loSHC8mX7GTW6gAnjh+D1Dcz3sUc8FAVDxFXZemg/mw/uY2xxKReOm8wJJWX2w8T0C6kEQC2q2iIiiEhQVTeIyMkZz5nJSarKwl1bmVexsdPgJxKJs39/E1VV9bS2Ou3zAwEr8TEDmDgjkas684tVVzcz+aRShpaFuj62n/K4kw+rKnvqa3l6zSLK8gq58ISTmFw2yiZsNVmVSgBUKSIlwGxgrogcAnZkMlMmN6kq83ds5I2dWykMtN/Lq21pj98nNneXGVQS3eaj0Thr11VTVpbHpEklA3qaFhGhIBBEVakLN/P3DcspDIS4cNwkTh8x1iZtNVnR5UCIx+wscjEwBHhBVVszlqsesG7wA5uq8u9t6yivqnhL8GOlPSZXqSqtrXFE4MQTSxg1qiDbWUqbcDRKOBahMBDihinnU5KXn+0smQGiLwdCRETOAi7EafT/en8LfszAFtc4L2xew8q9Oylyu9zG48rhw2H27Wu00h6TsxK9xWIxZfPmw+zb28jkyUPJy++7sYMyJejzEfT5aGwN8+TqhXx82gUUBgZudZ8ZeFKZDPV7OJOUlgHDgN+JyHcynTGTG2LxOP/YuJKVe3dS4A9x+FCYDRsO8uabu1m3roaDNS0EAs4YPl6ftRcwucnrFUJBDw2NEZYu2+f0dIynXnrfnxUEgjSEW3hq1SKaI/bb2vSdVOYC2whMVdUWdzkPWKGq/aohtFWBDTzReIy/rlnGyqpK6g5EOeiW9AjOYHFiPbmMeYt43KkWy8vzMnnyUIqLA9nOUlrUhZs5rqiU6848l4B34Jdwmczpyyqw3TgDILa4y0FsZnbTC+FwlEVLdvO39cs4RD2t9Rb0GJOqxNhBra0xVq48wOgB3mU+oSgQYnf9If62fhnXnDbDeoiZjOswABKR+3Ha/NQCa0Vkrrv8TmBx32TPDBY1Nc0sXbqXl1/exesLdzHq0jj5o4CwEAzaB50x3eX3e/H5nC7zBw40MWHCEEaOLBiwg56LCEWBENsO7uf5TSu58uRpKY38bkxPdVYClKhPWgr8LWn9vIzlxgwa0WicdetqWLRoNy+9tIOKilpEwJ8nTLzSQ6BMiDcLWImPMT12dKb5OJs3H6KqqoFJk0oYMqTvZppPJxGhOJjH2v1V5PkCvOPE06y3p8mYziZDfTzxXEQCwGR3caOqRjKdMTPwHDjQRHn5Xl5+eSdvvrmHSCRGLKYUFfkZOTIfjx9KLwrjK4k7wQ/2wWZMOni9HrweIRyOsmpVdVZmmk8XEaEomEf57u3k+f1ceMLkrg8ypgdSmQvs7Ti9wCpwvrGOF5EbbTJU09ISZf36GhYu3MPcuTvYtavOmVPIKwwZEsTvT/oVKsqQc1st+DEmU0SOVIsdPNjCwYMtjB1byPHHF/fJTPPplBhBesGOzeT5A5x93PhsZ8kMQqn8PPgZ8C5V3QggIpOBp4CzM5kx0/9EIjE2bjzI0qX7ePXVStaurUYEotGjpTztF1crxdMjBEfFiTVZ8GNMJiXPNL9rVwN79jQycWIJI0fkD6j/el6Ph4JAgJe2riXP7+e04WOynSUzyKQSAPkTwQ+Aqm4Skb6dqthkRSwWZ8uWwyxf7gQ8K1bsR9Vp35Of76OsLITX21UDZqXgtCih8VELfozpQ4neYrFYnE2bDlFVWc+kk0ooLh447YN8Hi/5vgBzNqwk5PUzceiIbGfJDCKpBEBLReRh4A/u8vUcbSBtBpnduxtYtGg38+dXsmTJXiKROLGYEgp5KSkJ4uvmYIR5E6MUnhIlZtVexmSF0z5IaQlHWbWymrJhIcaNK6agYGD8jvV7vcRV+cu6pXx0ynmMKS7NdpbMIJFKAHQL8Dngi+7yAuBXqSQuIu8GfgF4gYdV9e42278C/DcQBQ4An1TVHe62GLDa3XWnql6VyjlN9+3d28irr+5i9uwtbNlyCFUIBr0UFQXw+3veRT14XJSiqRFiLQJqwY8xWeO2D1KfUlPTQk11C3n5PkaPLmD48Pxe/T/vC0GfD40qz6xZzA1Tz2dEQXG2s2QGgU5HghYRL7BWVU/pdsLOsZtwxg2qBJYAH1HVdUn7XAIsUtUmEbkVeLuqXutua1DVwlTPZyNBd8/+/Y3Mn1/J7Nlb2LjxIAD5+X6Kivxp6XbqHxaj9MJWNAIas+DHmH5FlWhMiUWdoddLSoKMHl1AaWkITz8emqIp0krA6+MT0y+kKGjzhuWqPhkJWlVjIrJRRMap6s5upn0usEVVtwGIyNPA+4EjAZCqvpK0/0Lghm6ew3TDgQNNLFjgBD3r19cAkJ/v66Txcs/4iuOUXNCKRi34MaZfEsHnE3w+Z8b52towhw+H8XiEESPyGTkyn6Ki/jfFRr4/QEO4hWfXlXPDlPPxe21yZNNzqVSBleKMBL0YaEysTKFKagywK2m5EpjZyf6fAv6VtBwSkXKc6rG7VXV22wNE5GbgZoBx48Z1kZ3c1NjYyty5O/j737ewZk01qk7QM2JEfkZ+6Xny45RcFAZAoxb8GNPfiQiBgBNIxOPOyNJ79zYSCHg47rhChg/LJxjqP4FGQSDI3oZa/rl5JVedPN0GSjQ9lkoA9N1MZ0JEbgBmABcnrT5BVatEZCLwsoisVtWtycep6kPAQ+BUgWU6nwNJbW2YZ5/dyOOPr6WpKUpeXuaCngQJKKUXteLxQTxsH0rGDDQejzOyNKrEYkrF9jq2b6/D5xNCIR/5+T7y8/3k5fkIBrwEQ14Cfm+f9m8QEYoDIdbt38Pw/GIuGDep705uBpXO5gIL4TSAnoTTGPkRVY12I+0q4Pik5bG0M4mqiLwD+DZwsaqGE+tVtcr9u01E5gHTga1tjzfHOnCgiaee2sDTT28gEolRWhrsk2HxxaeUzgrjzYsTb+nfDSqNMV0QwesTvD5Albg6A582NUWIx5sQEUQg0YQ0GPASyjsaIHm9znYRwSOCeDhyTPJy4rnf7+3WYI0iQmEgyKs7NjIsv5DJw0Zl5j6YQa2zEqDHgQhOr68rgNOAL3Uj7SXASSIyASfwuQ74aPIOIjId+A3wblXdn7S+FGhS1bCIDANmAT/txrlzTlVVPU88sY7ZszcTjytDh4YIBPqokaCn7SjPxphBQwSP0GHpsaoSiyv19a3U1oZR5ciErMcc0cFHg6rz8Hic3qfBoJdQyEdeno9AwOs8/B4CAS++pN5qXo+HPJ+fv29czsdDsxhZaD3DTPd0FgCdpqpnAojII3RzBnhVjYrI54EXcbrBP6qqa0XkLqBcVecA9wCFwJ/detxEd/dTgd+ISBzw4LQBWtfuiXLctm2H+d3v1vDiixUAlJWFuj1WT+/YKM/G5DJn+ht6N92G6pFBVltb49TVthJXdUqNAJJKm0IhL0OHhigpCVFcHECI86c1i/nEWRdRGBg4gzya7OuwG7yILFPVszpa7m9yrRv8+vU1PPTQSl57rQoRYdiwVEZlTjelcGqE/BOjxC34McZkmKoSjylRt/s+CvkFPkqG+zmupISbz7+IwnwLgga7vugGP1VE6hLnA/LcZQFUVa28MQvWrq3m/vuXs3TpPrxeyXjD5g6JU/KTN95GeTbG9A1JbpuEExCFwzGqKqIcKG7mhhf/RFnNMC655ATOOmsEkycP7eMScTOQdBgAqWr/6fdo2LbtMPffv4wFC6rw+z2MHJmXve6fHmXIjAjBsTGr9jLGZI0kjWdEq1J2qrJ/UQ0///kBfD4PXq+H888/jve8ZwIzZ46moKD/jW1ksieVbvAmi6qq6nnwwZW88MJ2PB5h5MgslfgkeJWSma1umx+w4McY0z8I8RYYMVMJxEK07vURjcZ5/fUq5s+vRARmzRrDe94zgfPOO47CQguGcp0FQP1UdXUTjzyymr/+dTOqMHx4Xhba+BxLfErJBa34y6zkxxjTD8UFbYWSmREOvuKBOg/DhuUBEIvFWbhwt9tuEs47bzTvfe9Ezj9/TL8c9dpkngVA/UxtbZg//GEtf/jDeqLROMOG5fWLOmwJOOP8HO3qbsGPMab/0aggXqVkVisHXw4eGZTV6/VQVnY0GFqyZC9vvrkHETj3XCcYuuCC4ygutkbUucICoH6isbGVP/95Ew8/vJqWlihlZaEjw9NnmyeklF4YxltkwY8xpv+LhwVPKM6Q81o5tCAA8WM/s9oGQ8uW7WPRoj0ATJ06nHe84wTOOWcUEyYMsak2BjELgLKsoaGV55/fxoMPrqShoZXS0iClpf3nF4gnP87Qi1rx5Cnx5uyXRBljTCriLUKgLM6QcyLUlfs7nJjZ6/UwdKgzaGwspmzYcJAVK/bj8QhFRQEuvngsb3vb8UybNqJPRtU3fafDcYAGmoE2DtDOnXX86U8b+etfNxOJxBgyJEheXv+KR72FcUovCuMJOh8mxhgzsCiePCVW5+HQmwHiTd37EdfSEqW2thURiMfh5JNLeec7T+Dcc0czeXJp1ttl5qp0jQNkAVAfiseVJUv28sQTa1m8eC8iUFoa7DdVXcl8xXFK3xZGvDaxqTFmIFM8IdAYHH4zQKS6Z5+38bjS0BChsTGC1ysEg15mzRrDzJmjmTSphIkTS8jP96c576Y9FgC10Z8DoMbGVl58sYJHH13Dvn2N+HxOkWtWu7N3wlcap/TCsDP8fGv/zKMxxnSH+BXxQf0qH81bffS2LWNra4y6ulai0Tg+n4doNM7o0QWcccZwpk8fwUknlTJx4hBrVJ0BFgC10R8DoF276vjznzfxl79sorU1RmGhv3+PPSFK6PgYxdMjaBw0YsGPMWYQ8SjekNK800fdcj900C6oJ1SVlpYYjY0RIpE4fr8TFJWV5XH66WWcffZIJk0qZezYIkaMyO8XvXsHqr6YCsP0QGNjK8uX7+eZZzawcKHTq6BPZ2bvIf/QGEXTI/iK42irdNhg0BhjBqy4EGuC0LgovuI4h98MpK1zh4iQl+c7pi1nYqqOhQv38Oqrlfh8Hmc+s7gyalQB48cPYfLkUsaPH8JxxxVw3HGFjBiRb22L+ogFQL2kqmzfXsuSJXuZO7eCVauqEQGPJ4vzdHWDJz9O4RkRQmNiEBfr5m6MGeSEeBN4i5Syy8JOu6CazLTDFBFCIR+h0LFftfG40twcZfnyfSxcuNuZ48wrxOOKKowalc8JJzjB0bhxxYwcmc+IEfmMHJlv03mkkQVAPZAo5VmwoJKXX95FbW0YVSUU8jF8eF6/D3oAxKvknxSl4JQogAU+xpgcImjYaRdU+rZW6lf6aN7W+3ZBqfJ43lpalBCPK01NR4Mj4Eh1WSQSJz/fx6hRhRx/fCEnnljC2LFFjBxZwIgReYwYkU9+vt/GLkqRBUApSJTyLFq0h7lzd7BmjVPKo6oMGRJk5Mj8bGexG5TgmBhFUyNO9/YwbxkkzBhjcoFGBI0pxdMi+EuUuhX+rH8edhYcqSqxmFJd3URVVT2vvurMcZaoMotGnbZHw4blMXJkPqNHFzJ2bBGjRhUwdGiIsrIQQ4fmuc0y+l/v475mAVAK/vWv7Xzve68dKc4cKKU8bflK4hRNayUwNE68NVHdZYwxOSzRLuiEKL4hcWrLA8Tq+2eJuIjg8wk+n4eCgva73MdicZqbo2zefIi1a2tobY3h8ThVbCJONVs0GqewMMDQoSFKS4OUlIQYOjTEsGF5lJaGKC4OUFQUoLDQ+VtU5HTgycvzDarSJQuAUtDcHEUVRo8uyHZWesQTUgpPj5A3LobGsYlMjTHmGEK8GbzFStk7Wog1eGiu8BLe7SXWOLAaJHu9HvLyPJ0OrKuqRKNKXV2YgwdbiMUOEY3GiUadBtper+D1etyajqMlTwD5+X4KC/0UFQUYMiTAkCFBSkqClJaGKC0NUVDgp6DACZimTh3+lvZP/Un/zZnpOY/iH6L4hsYIjo4TGBYHUWLWzscYYzrgtAtSwBNUCs+IUHRGlGi90FThpXW3l1g3R5Lur0QEv1/w+7t3PYlAKBKJceBAE3v3NhCNKrFYnFjM2eb1evB4nPZK9957CRdffHyGrqL3MhoAici7gV8AXuBhVb27zfYg8ARwNlADXKuqFe62bwKfAmLAF1X1xUzmdUDzKv6SOP6yOMFRcfxD4856UTQmTjsfHRz/cY0xJrMEjTqzyjujSCtFZ0bgzCjROnFKhvZ4uz2txmCQXAXXlQMHmonH+/c4gxkLgETEC/wSeCdQCSwRkTmqui5pt08Bh1R1kohcB/wEuFZETgOuA04HjgP+LSKTVTWWqfwOHIr4wV8Sx1cWJzQ6hm+I+ybzKBpNBDxW2mOMMb1zbDDkzVeKpkQomuIGQ9u9RGs9TpvKsKAR3M9eMxBksgToXGCLqm4DEJGngfcDyQHQ+4E73efPAg+I08Lq/cDTqhoGtovIFje9NzOY3w4pijcP1J8Uf3XyHpfEPwKIIpK8zDHLAuAB8Skev9Mt0+N35q7xBJ2iWG9QkQB4Aop4QTygced4jUAsjFNu6+b22L/GGGPSQSNABI4EQ1PjRze63wkadeZPjLcIsWYh1gixFidAirfiDDKrgDrta448j9NmvUC8i0/yfvwxL4F41ztlWSYDoDHArqTlSmBmR/uoalREaoEyd/3CNseOaXsCEbkZuNldbBCRjenJ+rH8ecXDvQHv2EB+4ZG3W0oxfqo/BESO7pp42tGxmvRvDgk3NEiwsDDnrru/sPufXXb/s6sb91/a/ex2AhtNPHXWdZFcCmfrz28IAX3nuw/uiLU2Hk5TksOAavf5CelIcEA3glbVh4CH+uJcIlLefPhgr+ceMT0jIuVNh2rs/meJ3f/ssvufXXb/s09EytMx/1eyTLbiqgKSm3+Pdde1u4+I+IAhOI2hUznWGGOMMaZHMhkALQFOEpEJIhLAadQ8p80+c4Ab3efXAC+rMz39HOA6EQmKyATgJGBxBvNqjDHGmBySsSowt03P54EXcbrBP6qqa0XkLqBcVecAjwC/dxs5H8QJknD3+xNOg+ko8Ll+0AOsT6raTIfs/meX3f/ssvufXXb/sy/tr4FoVw2xjDHGGGMGmdwbyckYY4wxOc8CIGOMMcbkHAuAXCJyvIi8IiLrRGStiHypnX1ERO4TkS0iskpEzspGXgerFF+Dt4tIrYiscB/fy0ZeByMRCYnIYhFZ6d7//2lnn6CIPOP+H1gkIuOzkNVBKcX7f5OIHEh6//93NvI6mImIV0SWi8g/2tlm7/8M6+L+p/X9P6DHAUqzKPBVVV0mIkXAUhGZ22bqjitweqSdhDOo46956+COpudSeQ0AFqjqlVnI32AXBi5V1QYR8QOvici/VDV5UNJ2p6/JRmYHoVTuP8Azqvr5LOQvV3wJWA8Ut7PN3v+Z19n9hzS+/60EyKWqe1R1mfu8HucFaDv69PuBJ9SxECgRkdF9nNVBK8XXwGSI+75ucBf97qNtL4n3A4+7z58FLnOnrzG9lOL9NxkkImOB9wIPd7CLvf8zKIX7n1YWALXDLdacDixqs6m96T3sCzoDOnkNAM53qwn+JSKn923OBje3+HkFsB+Yq6od/h9Q1SiQmL7GpEEK9x/gQ24V/LMicnw7203P/Rz4OtDRRFb2/s+sn9P5/Yc0vv8tAGpDRAqBvwBfVtW6bOcnF3XxGiwDTlDVqcD9wOw+zt6gpqoxVZ2GM/r6uSJyRpazlFNSuP/PAeNVdQowl6OlEaaXRORKYL+qLs12XnJRivc/re9/C4CSuPXufwH+qKp/bWcXm6Ijw7p6DVS1LlFNoKr/BPw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Test indices: 1425.802.705.101.902Test indices: 1425.802.705.101.902
Train indices: 1015.802.705.101.902Train indices: 1015.802.705.101.902
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\n", 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EePLgzlGTmoOSPBb4CeD1SwhMkiRJUiuK3bVr0kGMTVWduK/njJrUXEBvwP+nq2pLkscDX9zXi0mSJEkav1p8crBOSfI9wC8CP9Ls+hRwYVV9Z2/njDpRwAeBD/Zt3wqcsfRQJUmSJI1DFdPW/eydwG7gHc32S5t9P7u3E0adKOAJTUXfV1UnJDkROK2q3rS8eCVJkiQtT03VRAHA7EAXtCuHjecfdULri+hNvfxdgKq6nt7LNCVJkiRN2J4R/tch320aVQBI8kSaPGRvRh1T87Cq+kLygAmwp2c0kiRJktRRNX0tNa8GNie5nd7LN48BfnqxE0ZNau5OclxTKUnOBO5cepySJEmSxmXKZj+7qmmpeWKz65aqunexc0ZNal4BbAB+MMkdwJeBlyw5UkmSJEljMW3vqUky2CpzUhKq6j17O2fU2c9uBZ6X5FB643C+SW9MzYPe5ilJkiRp/yp2TzqEcTqpb/1Q4PnANcDSkpokh9NrpTkC+DDwN832rwDXA3+yvHglSZIkLUtNV0tNVb2qfzvJI4APLXbOsJaa9wFfBT4LvBx4PRDgRVV17ZIjlSRJkjQWBdM2UcADVNU9SVYlmamqBZukhiU1j6+qJwMkeTe9yQGOrqpvjzlWSZIkSUtSXZuyeZ9V1Y8udnzYe2rumw+6yYp27EtCk2Rdkm1Jtic5b5FyZySpJLOj1i1JkiSpN1HA7j27hi7TbFhLzVOSfL1ZD/DQZjtAVdXhezsxyQxwIb2BPTuALUk2VtVNA+UOA34J+PwSP4MkSZK0otWUt9QMs2hSU1Uzy6j7ZGB7M3MaSS4FTgduGij3RuAtwOuWcS1JUou+dM8fTDoETdBxj/jlSYcgaYhpmihgKYZ1P1uOI4Db+7Z3NPvuk+TpwFFV9ZHFKkqyPslckrmdO3eOP1JJkiSps4piz9BlmrWZ1CwqySrg9+hND72oqtpQVbNVNbtmzZr2g5MkSZI6oqrXUjNsGUVXx8SP9PLNJboDOKpv+8hm37zDgBOAK5MAfD+wMclpVTXXYlySJEnSFCl21/InAujymPg2W2q2AMcnOTbJQ4CzgI3zB6vqnqpaXVXHVNUxwOcAExpJkiRpH8y/p2bYMoL7xsRX1b3A/Jj4QfNj4g+Y17y0ltRU1S7gXGAzcDPwgaramuSCJKe1dV1JkiRpZSn21PAFWD0/Tr1Z1g9UNLYx8ftbm93PqKpNwKaBfefvpewpbcYiSZIkTaUaefazu6tqyWNg+sbEn7PUOtrSalIjSZIkqV0F8y0xy9XZMfEmNZIkSVLH7WEsSc19Y+LpJTNnAS+eP1hV9wCr57eTXAm8dtIJDZjUSJIkSZ1WFLv27F5+PVW7ksyPiZ8BLp4fEw/MVdXGxWuYHJMaSZIkqePG1P2ss2PiTWokSZKkDuu9fHM8SU1XmdRIkiRJnVajzn42tUxqJEmSpA4b4+xnnWVSI0mSJHXcmGY/6yyTGkmSJKnDxjX7WZeZ1EiSJEld5kQBJjWSJElSlzmmxqRGkiRJ6jhnPzOpkSRJkjrOlhpJkiRJnVXArlrZEwWsarPyJOuSbEuyPcl5Cxz/+SQ3JLk2yaeSrG0zHkmSJGnaVBV7RlimWWtJTZIZ4ELgVGAtcPYCScv7q+rJVfVU4K3A77UVjyRJkjStTGraczKwvapurap7gUuB0/sLVNXX+zYPhRX+1iBJkiRpCVZ6UtPmmJojgNv7tncAzxgslOQVwGuAhwDPXaiiJOuB9QBHH3302AOVJEmSuqo3pfPKnv2s1TE1o6iqC6vqOOC/Ab++lzIbqmq2qmbXrFmzfwOUJEmSDmiOqWmzpeYO4Ki+7SObfXtzKfDOFuORJEmSpk4V7NpjS01btgDHJzk2yUOAs4CN/QWSHN+3+R+BL7YYjyRJkjR1Cthdw5dp1lpLTVXtSnIusBmYAS6uqq1JLgDmqmojcG6S5wHfBb4K/HRb8UiSJEnTatq7lw3T6ss3q2oTsGlg3/l967/U5vUlSZKkaTffUrOStZrUSJIkSWpZwR6TGkmSJEldVcB3V3hWY1IjSZIkdZjdz0xqJEmSpM5b4Q01JjWSJElSl1UVu539TJIkSVKXrfB3b5rUSJIkSV3mmBqTGkmSJKnTqpz9zKRGkiRJ6rgVntOY1EiSJEldZvczkxpJkiSp8/Y4+5kkSZKkrrKlxqRGkiRJ6rZyTM2qNitPsi7JtiTbk5y3wPHXJLkpyfVJPpbkcW3GI0mSJE2bAr67u4Yu06y1pCbJDHAhcCqwFjg7ydqBYtcAs1V1InAZ8Na24pEkSZKm0Xz3s2HLNGuzpeZkYHtV3VpV9wKXAqf3F6iqK6rqm83m54AjW4xHkiRJmj5N97Nhyyi62tOqzaTmCOD2vu0dzb69eRnwlwsdSLI+yVySuZ07d44xREmSJKnbxtVS0+WeVq2OqRlVkpcCs8DbFjpeVRuqaraqZtesWbN/g5MkSZIOcHuqhi4j6GxPqzZnP7sDOKpv+8hm3wMkeR7weuA5VfWdFuORJEmSpk5R3Dta/7LVSeb6tjdU1Ya+7YV6Wj1jkfr22tNqf2szqdkCHJ/kWHrJzFnAi/sLJHka8MfAuqq6q8VYJEmSpKlUBbv3jFT07qqaHcc1+3paPWcc9S1Xa0lNVe1Kci6wGZgBLq6qrUkuAOaqaiO97mYPBz6YBOAfquq0tmKSJEmSpk1vTM1YpjfrbE+rVl++WVWbgE0D+87vW39em9eXJEmSVoLd43n7Zmd7WrWa1EiSJElqV9V4Wmq63NPKpEaSJEnquBHH1AzV1Z5WJjWSJElSh+2p4t5xZTUdZVIjSZIkddyYJgroLJMaSZIkqcOK8XU/6yqTGkmSJKnDxjilc2eZ1EiSJEldNqbZz7rMpEaSJEnqsF73M5MaSZIkSR1VVdy726RGkiRJUkc5psakRpIkSeo8u59JkiRJ6qxeS82ko5gskxpJkiSpy8qWGpMaSZIkqcP2UNy7Z2W/fXNVm5UnWZdkW5LtSc5b4Pizk/xtkl1JzmwzFkmSJGkqFezeM3yZZq0lNUlmgAuBU4G1wNlJ1g4U+wfgHOD9bcUhSZIkTbP52c+GLdOsze5nJwPbq+pWgCSXAqcDN80XqKrbmmNTnjtKkiRJ7VnpY2ra7H52BHB73/aOZt8+S7I+yVySuZ07d44lOEmSJGkaVNlS0+qYmnGpqg1VNVtVs2vWrJl0OJIkSdIBZc+e4cs0a7P72R3AUX3bRzb7JEmSJI1JVbFn15RnLUO0mdRsAY5Pciy9ZOYs4MUtXk+SJE2h73vHaZMOQRP0T6/aOOkQOqEcU9OOqtoFnAtsBm4GPlBVW5NckOQ0gCT/LskO4MeBP06yta14JEmSpGlVVUOXadbqyzerahOwaWDf+X3rW+h1S5MkSZK0FGVLTatJjSRJkqR2FWVSM+kAJEmSJC2DLTUmNZIkSVLXOfuZJEmSpO6ypcakRpIkSeoyx9SY1EiSJEndZkuNSY0kSZLUddP+HpphTGokSZKkLisnCjCpkSRJkjrMMTUmNZIkSVLnmdRIkiRJ6i4nCjCpkSRJkrrOpEaSJElSd9lSY1IjSZIkdVlR7Nm9smc/W9Vm5UnWJdmWZHuS8xY4/j1J/qw5/vkkx7QZjyRJkjR1mpaaYcsouvr9vbWkJskMcCFwKrAWODvJ2oFiLwO+WlU/APw+8Ja24pEkSZKm1TiSmi5/f2+zpeZkYHtV3VpV9wKXAqcPlDkdeE+zfhnwH5KkxZgkSZKkqTOmlprOfn9vc0zNEcDtfds7gGfsrUxV7UpyD/AY4O7+QknWA+ubzX9Jsq2ViDXMagb+blaUyf//daXz/tMkrez7j1dPOoCVbkXff/mliT7/HjfJi49s579u5l2fXz1CyUOSzPVtb6iqDX3bY/v+vr91YqKA5g97w9CCalWSuaqanXQcWpm8/zRJ3n+aJO8/DVNV6yYdw6S12f3sDuCovu0jm30LlklyEPAI4J9bjEmSJEnSwjr7/b3NpGYLcHySY5M8BDgL2DhQZiPw0836mcDHq2plT7ItSZIkTUZnv7+31v2s6WN3LrAZmAEurqqtSS4A5qpqI/C/gfcl2Q58hd4fnA5cdgHUJHn/aZK8/zRJ3n/aL7r8/T0HQGIlSZIkSUvW6ss3JUmSJKltJjWSJEmSOs2kpsOSnJPk34xQ7pIkZy6h/p9P8lML7D8myY3N+lOTvKDv2BuSvHaEupPk40kO39e4Fqjrb5I8arn1aDTLve+Wej+OcL3/3rd+3z06wnm/vNB9voTrn5vkZ5dbj8YjySlJ/mLU/WO43gv737qd5MokQ6fgTfLYccSTZE2Sv1puPRqvpd5vSf5Nksv2cuy+e8vnnnQ/k5puOwcY+uVyqarqXVX13iHFngq8YEiZhbwAuK6qvr6Ecwe9D/jFMdSj0ZxDi/fdMvz34UUeqJmK8meB94/h+hcDrxxDPeqmFwJrhxVawGuAi5Z78araCdyZ5FnLrUuTV1X/WFWj/Pjjc09qmNQcIJpfWP4uyZ8kuTnJZUke1hw7KcknklydZHPzy96ZwCzwJ0muTfLQJOcn2ZLkxiQbkr2/gjzJ9ya5ull/SpJKcnSz/aUkD+tvdWliuC7JdcArmn0PAS4AfrKJ4Seb6tc2vyTdmuRVewnhJcCH++L5qSTXN9d4X7PvkiTvTPK5pq5Tklzc/Plc0lfXRuDsffwjF/v/vlvg+g+6RrP/yiRvSfKFJLck+ZFm/8OSfCDJTUkuT/L5JLNJ3gw8tInpT5rqZ5JclGRrkr9O8tAFQngu8LdVtaup/wfSa/m7LsnfJjmuue8+keTDzX345iQvaWK7IclxAFX1TeC2JCcv8a9jRUlyaJKPNH/WN84/P4bcE29v/o5vnP9zTnJyks8muSbJZ5I8cR9juLj5u7wmyenN/nOSfCjJXyX5YpK39p3zsuae/EJzf/1hkh8CTgPe1sR3XFP8xwfv4QWcAfxVU/dMkt9pPt/1SV7Z7L8tyW83dc8leXrzZ/OlJD/fV9f/o/ds1YgmdR821zyxWb8myfnN+gVJXp4H9oh4aJJL03tGXw48tNnvc0/qV1UuB8ACHAMU8Kxm+2LgtcDBwGeANc3+n6Q3vR7AlcBsXx2P7lt/H/CfmvVLgDMXuOZW4HDgXHrzkr8EeBzw2eb4G4DXNuvXA89u1t8G3NisnwP8YV+db2ji/R5gNb2XMR28wLX/HjisWX8ScAuwuv9zNHFfCgQ4Hfg68GR6yfjVwFP76vsi8JhJ/z12bZnQfXcJvXnth13jd5v1FwB/06y/FvjjZv0EYNd8LMC/DHyuXfP3CPAB4KULxPI/gVf2bX8eeFGzfgjwMOAU4GvAY5v7+g7gfzZlfgn4g77zXw/8yqT/Xruw0Psyf1Hf9iNGuCcuatafzf3PoMOBg5r15wH/t1k/BfiLBa57337gt+bvC+CR9J5Dh9J7rt3axHQIvefVUfRaKG8DHt3E+kma59/g/b63e3gglmOBq/u2fwG4rO/zzD8LbwN+oVn/fXrP48OANcA/9Z1/BHDDpP9uu7RM8D48j94PhI+g9+/v5mb/FcAT6T3D5ut+Td/1T8TnnovLgktr76nRktxeVZ9u1v8P8Cp6v+CdAHw0vR/AZ4A793L+jyb5VXoPpEfTS1r+fJHrfQZ4Fr0H828B6+glEJ/sL5TkkcAjq+qqZtf7gFMXqfcjVfUd4DtJ7gK+D9gxUObRVfWNZv25wAer6m6AqvpKX7k/r6pKcgO9f7xvaGLaSu8Bfm1T7i56Xzgm/kbbDtrf9928Jw65xoea/15N7+8a4IeBtwNU1Y1Jrl+k/i9X1bUL1NHvscDNAEkOA46oqsub+r/d7AfYUlV3NttfAv66Of8G4Ef76rsL+MFFYtL9bgB+N8lb6H3p+2SSE1j8nvhTgKq6KsnhzbPpMOA9SY6nl6AfvA8x/BhwWu4fB3gIcHSz/rGqugcgyU30fvBZDXxi/hmV5IPAExapf6F7uN9jgZ19288D3lXNL+gDz8L5l9/dADy8eX5+I8l3kjyyqr7G/c9BjW5S9+En6T1rvwx8BHh+eq3kx1bVtiTH9JV9NvCO5prX+9yTFmZSc2AZfGlQ0UsytlbVMxc7MckhwB/R+/Xm9iRvoPcP9GKuAn6E3j/WHwb+W3PNj+x76A/wnb713Sx8n+1Ksqqq9oxY156BevcM1HsI8K19DVTA/r/v7jt9yDXm/773dg8NM3gfLtQN41uMFu/gvdd/X3ofLkFV3ZLk6fRaMd6U5GPA5Sx+Tyx0r74RuKKqXtR8EbxyH8IIcEZVbXvAzuQZjPYcG2bYPTzq/ddf12LPQu+/fTTB+3ALva68twIfpZcwv5xeIrIcPve0Yjmm5sBydJL5h+iLgU8B24A18/uTHJzkSU2Zb9D7dQjuf0DdneTh9Lr3DPNJ4KXAF5vk4iv0Huyf6i/U/AL4tSQ/3Ozq77PdH8O+2AY8vln/OL2+548BSPLofakovZ+Uvp9eFw3tu/19381b7Bp782ngJ5rya+l1R5z33ST78is99H6t/AGA5pfvHUle2NT/Pc0vp/viCcBIsw+tdOnNoPfNqvo/9Lq0Pp3h98T8eIcfBu5pWlIeQa9rDPS6je2LzcArm2cISZ42pPwW4DlJHpXeYOsz+o4t5Vl4Cw/8Jf2jwM81de/zsxDvv302qfuwqu4Fbgd+HPgsvX+PX0vvx8ZBV9F7NtO0Ip3Yd8znntQwqTmwbANekeRm4FHAO5sH35nAW9IbpH8t8ENN+UuAdyW5lt4vKBfRe7BspveP76Kq6jZ6v1TOP0Q/BXytqr66QPGfAS5srtU/EPwKehMD9E8UMIqP0OuzS1VtBX4T+ETzGX9vH+oBOAn43HyXDe2z/XrfzRtyjb35I3pfNm4C3kSvq9s9zbENwPW5f8DsKP6SXteOef8FeFXTveMz9JLlffEsel9MNdyTgS8099FvAG8a4Z74dpJrgHcBL2v2vRX47Wb/vramvJFeN6Hrmy6tb1yscFXdQa+r7hfoJdi3cf/9dynwuvQGfR+3cA0Pqu9fgS8l+YFm17uBf2jiuY7mi+w++FGW39K+0kzyPvwkcFdVfatZP5KB7t+NdwIPb57RF/DA1hyfe1IjVYOtqJqEprn6L6rqhEnHsj+kN5PMe6vq+WOo6+3Axqr62PIjW1m6dt8lmaE38cS3my+OfwM8sfkSstQ6Lwd+taq+uMzYnga8pqr+y3Lq0cKSXElv4pK5Ccfx8Kr6l6Y15XJ6A7gvX0Z9LwJOqqpfH0NsVwGn7+WHKY3BgXIfLpfPPU0jx9RoIqrqzvSmnTy8lv+umhtNaFaMhwFXNN0tAvzichKaxnn0Bs4u6x93en3i/8cy69CB7w1Jnkev6+Vf05tGecmq6vL5rrfLkWQN8HsmNBqRzz1NHVtqJEmSJHWaY2okSZIkdZpJjSRJkqROM6mRJEmS1GkmNZJ0gEmyu5kmfX45Zgl1vLB5l48kSVPP2c8k6cDzrap66jLreCHwF8BNo56Q5CDf9yRJ6iJbaiSpA5KclOQTSa5Osrl51xNJXp5kS5LrkvzfJA9L8kPAacDbmpae45JcmWS2OWd1ktua9XOSbEzyceBjSQ5NcnGSLzQvkjy9KfekZt+1Sa5Pcvxk/iQkSXowkxpJOvA8tK/r2eXNe3n+F3BmVZ0EXAz8ZlP2Q1X176rqKcDNwMuq6jPARuB1VfXUqvrSkOs9van7OcDrgY9X1cn03lD/tiSHAj8PvL1pQZoFdoz3I0uStHR2P5OkA88Dup8lOQE4AfhoEoAZ4M7m8AlJ3gQ8Eng4sHkJ1/toVX2lWf8x4LQkr222DwGOBj4LvD7JkfQSqeW+tE+SpLExqZGkA1+ArVX1zAWOXQK8sKquS3IOcMpe6tjF/a3zhwwc+9eBa51RVdsGytyc5PPAfwQ2Jfm5qvr46B9BkqT22P1Mkg5824A1SZ4JkOTgJE9qjh0G3Nl0UXtJ3znfaI7Nuw04qVk/c5FrbQZemaZJKMnTmv8+Hri1qt4BfBg4cVmfSJKkMTKpkaQDXFXdSy8ReUuS64BrgR9qDv8P4PPAp4G/6zvtUuB1zWD/44DfAX4hyTXA6kUu90bgYOD6JFubbYCfAG5Mci29rnDvHcNHkyRpLFJVk45BkiRJkpbMlhpJkiRJnWZSI0mSJKnTTGokSZIkdZpJjSRJkqROM6mRJEmS1GkmNZIkSZI6zaRGkiRJUqf9/7uBp2/lP7FXAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Datasets Size Comparison

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Verify Test dataset size comparing it to the Train dataset size.

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size11238
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Accuracy1.00Accuracy1.00
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Recall - Macro Average1.00Recall - Macro Average1.00
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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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vXp2WLVt6OyyTDtw5PfayiLwP+AH5415U1SYei8oYk2XExsYyYcIEhg8fzvXr1/nggw8ICgrydlgmHbmTKKYB3wGtcJwq+wRw2pNBGWOyBlWldevWLFy4kMaNG/PVV19RoUIFb4dl0pk7iaKEqk4UkecSDEdt9HRgxpjMKyYmJr6I3yOPPEL79u3p27cvN1RwMNmEO6fHXnf+PC4iLUUkGLjDgzEZYzKxXbt2UbduXebMmQPAwIED6devnyWJbMydRPGmiBQFXgCGAROAIZ4MyhiT+Vy7do2RI0dSvXp1Dh06RJ48djubnCLF37Sqznc+PQc0BhCRBz0ZlDEmc9m4cSO9e/cmIiKCxx57jI8//piSJUt6OyyTQVxdcJcb6IKjxtMiVd0lIq2Al4ECQHDGhGiM8baDBw9y7tw55s+fb6e85kCuehQTgXuBDcAYETkGhAAjVPX7DIjNGONFS5cu5fDhw/Tq1YuuXbvSunVrbr/9dm+HZbzAVaIIAQJVNVZE8gMngAqqeiZjQjPGeMPZs2cZPnw4EyZMIDAwkMcff5zcuXNbksjBXE1mX1PVWABVjQIOWZIwJnsLDw/Hz8+PSZMm8eKLL1oRPwO47lFUFZEdzucCVHAuC6CqGujx6IwxGWbfvn20a9cOf39/5s2bR0hIiLdDMpmEq0Rh95wwJptTVbZs2UKNGjWoUqUKCxcupEmTJtx2223eDs1kIskOPanqn64eGRmkMSb9HTlyhNatWxMaGsqWLVsACAsLsyRhbuLOBXdpJiJhIrJPRA6IyIhk2nQRkd0iEiEi0z0ZjzHGUcTvyy+/xM/Pj2XLlvHRRx9ZET/jkscurXRehzEWaAZEAhtFJFxVdydoUwn4P+BBVf1HREp5Kh5jjGOoqWXLlixatIiHHnqI8ePH88ADD3g7LJPJudWjEJECIlIllduuCRxQ1UOqeg2YAbS9oc2TwFhV/QdAVU+lch/GGDfExMSgqogIrVu3ZuLEifz000+WJIxbUkwUItIa2AYsci5XE5FwN7ZdBjiSYDnS+VpClYHKIvKriKwTkTC3ojbGuG379u3UrFmT2bNnA9C/f3/69OljRfyM29zpUYzE0Ts4C6Cq24D0+jMkD1AJaAR0B74SkWI3NhKRp0Rkk4hsOn3aboVhjDuuXr3Kv/71L0JCQjhy5IhNUps0c6vMuKqeu+E1deN9R3GUAIlT1vlaQpFAuKpeV9Xfgf04EkfinamOV9UQVQ2588473di1MTnb+vXrCQ4O5s0336R79+7s2bOHtm1vHPk1xj3uJIoIEXkUyC0ilUTkU2CNG+/bCFQSkQdE5DagG3DjkNX3OHoTiEhJHENRh9yM3RiTjD///JNLly7x448/MmXKFEqUKOHtkEwW5k6iGITjftlXgek4yo0PSelNqhoNDAQWA3uAmaoaISKjRKSNs9li4IyI7AaWAcOtTIgxabNkyRImTZoEQOfOndmzZw9hYTbtZ26dqLoeRRKR6qq6JYPiSVFISIhu2rQpbe/tVwyATRPOpl9AxnjZP//8w7Bhw5g0aRJBQUFs3rzZ6jOZm4jIZlVNU10Wd3oUH4jIHhF5Q0T807ITY4xnzJ07F19fX77++mtGjBhhRfyMR7hzh7vGInIXjpsYjRORIsB3qvqmx6MzxiRr7969dOzYkaCgIBYsWED16tW9HZLJpty64E5VT6jqGOAZHNdUvObJoIwxSVNVNm7cCEDVqlVZtGgRGzZssCRhPMqdC+58RGSkiOwE4s54KuvxyIwxiRw+fJgWLVpQq1at+CJ+zZs3J2/evF6OzGR37tR6mgR8Bzysqsc8HI8x5gaxsbF88cUXjBgxAlVlzJgxVKtWzdthmRzEnTmKOhkRiDHmZqpKWFgYP//8M82bN2fcuHGUK1fO22GZHCbZRCEiM1W1i3PIKeE5tHaHO2M8LDo6mty5cyMitG/fnscee4yePXtafSbjFa56FM85f7bKiECMMQ5bt26lb9++jBgxgi5duvDss896OySTw7m6w91x59P+Sdzdrn/GhGdMzhEVFcUrr7xCaGgox44do2DBgt4OyRjAvdNjmyXx2iPpHYgxOdnatWupVq0ab731Fo8//ji7d++mVSvrzJvMwdUcxbM4eg7lRWRHglWFgV89HZgxOcmRI0eIiopi8eLFNG/e3NvhGJOIqzmK6cCPwNtAwvtdX1DVvz0alTE5wOLFi4mMjKRv37507tyZ1q1bU6BAAW+HZcxNXA09qar+AQwALiR4ICJ3eD40Y7Knv//+m169ehEWFsbYsWOJiYlBRCxJmEzLVaKY7vy5Gdjk/Lk5wbIxJpXmzJmDr68v33zzDa+88gpr1qyxIn4m00t26ElVWzl/2t3XjUkHe/fupXPnzgQHB7No0SK7utpkGe7UenpQRG53Pu8hIh+KyH2eD82YrE9VWbduHeAo4vfzzz+zfv16SxImS3Hn9NgvgMsiEgS8ABwEpno0KmOygT/++IOHH36YOnXqxBfxa9q0KXnyuFNizZjMw51EEa2O2+C1BT5T1bE4TpE1xiQhJiaGMWPG4O/vz9q1axk7dqz1IEyW5s6fNhdE5P+Ax4H6IpILsLrGxiQhrojfkiVLCAsLY9y4cdx3n43UmqzNnR5FV+Aq0EdVT+C4F8X7Ho3KmCwmOjoaVUVE6NSpE1OmTGHhwoWWJEy2kGKicCaHaUBREWkFRKnqFI9HZkwWsWXLFkJCQpg5cyYATz/9NI8//rhVejXZhjtnPXUBNgCdcdw3e72IdPJ0YMZkdleuXGHEiBHUrFmTkydPUqhQIW+HZIxHuDNH8QoQqqqnAETkTmAJMNuTgRmTmf3666/07t2b3377jb59+/L+++9TvHhxb4dljEe4kyhyxSUJpzO4N7dhTLZ1/PhxoqOjWbJkCU2bNvV2OMZ4lDuJYpGILAa+dS53BRZ6LiRjMqcff/yRyMhInnzySTp27EirVq3Inz+/t8MyxuPcmcweDowDAp2P8ar6kqcDMyazOHPmDD179qRFixaMGzcuvoifJQmTU7i6H0UlYDRQAdgJDFPVoxkVmDHepqrMmjWLgQMH8s8///Daa6/x8ssvWxE/k+O4GnqaBEwBVgKtgU+BDhkRlDGZwd69e+nWrRs1atRgyZIlBAYGejskY7zCVaIorKpfOZ/vE5EtGRGQMd4UV8SvTp06+Pj4sGTJEho0aGD1mUyO5mqOIr+IBItIdRGpDhS4YdmYbOXQoUM0a9aMunXrxhfxa9KkiSUJk+O5+h9wHPgwwfKJBMsKNPFUUMZkpLgifq+++iq5c+fmiy++sCJ+xiTg6sZFjTMyEGO8QVVp3rw5S5cupUWLFnz55Zfce++93g7LmEzF+tQmR7p+/Tp58uRBROjWrRt9+vTh0UcftfpMxiTBo1dYi0iYiOwTkQMiMsJFu44ioiIS4sl4jAHYuHEjNWrU4LvvvgPgySef5LHHHrMkYUwyPJYoRCQ3MBZ4BPAFuouIbxLtCgPPAes9FYsxAJcvX2b48OHUrl2bv//+m2LFink7JGOyBHeqx4rzXtmvOZfvE5Gabmy7JnBAVQ+p6jVgBo675N3oDeBdICoVcRuTKqtXryYoKIjRo0fTr18/IiIiCAsL83ZYxmQJ7vQoPgfqAN2dyxdw9BRSUgY4kmA50vlaPOdptveq6gJXGxKRp0Rkk4hsOn36tBu7NiaxkydPoqosXbqUcePGUbRoUW+HZEyW4U6iqKWqA3D+xa+q/wC33eqOnbdU/RB4IaW2qjpeVUNUNeTOO++81V2bHGLBggWMHz8egI4dOxIREUHjxnYynzGp5U6iuO6cb1CIvx9FrBvvOwokPM+wrPO1OIUBf2C5iPwB1AbCbULb3KrTp0/z2GOP0apVKyZMmEBMTAwA+fLl83JkxmRN7iSKMcBcoJSI/BtYDbzlxvs2ApVE5AERuQ3oBoTHrVTVc6paUlXLqWo5YB3QRlU3pfYgjAHHNREzZszA19eXWbNmMXLkSFavXm1F/Iy5RSleR6Gq00RkM9AUEKCdqu5x433RIjIQWAzkBiapaoSIjAI2qWq46y0Ykzp79+7l0UcfJTQ0lIkTJ+Lv7+/tkIzJFlJMFCJyH3AZ+CHha6p6OKX3qupCbrjJkaq+lkzbRiltz5gbxcbGsmbNGurVq4ePjw9Lly6lfv361oswJh25M/S0AJjv/PkLcAj40ZNBGeOOAwcO0LRpU+rXrx9fxK9Ro0aWJIxJZ+7c4S5AVQOdPyvhuD5iredDMyZpMTExjB49moCAALZs2cJXX31FcHCwt8MyJttKda0nVd0iIrU8EYwxKVFVmjVrxrJly2jTpg2ff/45ZcqUSfmNxpg0c2eO4vkEi7mA6sAxj0VkTBISFvF79NFHefrpp+nSpYvVZzImA7gzR1E4wSMfjrmKpEpxGOMRGzZsIDg4mG+//RaAfv360bVrV0sSxmQQlz0K54V2hVV1WAbFY0y8y5cv869//YuPP/6Ye+65hxIlSng7JGNypGQThYjkcV4L8WBGBmQMwMqVK+nduzeHDh3imWee4d1336VIkSLeDsuYHMlVj2IDjvmIbSISDswCLsWtVNX/ejg2k4P99ddf5MqVi+XLl9OwYUNvh2NMjubOWU/5gTM47pGtOK7OVsAShUlX4eHhHDt2jGeeeYYOHTrQsmVLq89kTCbgajK7lPOMp13ATufPCOfPXRkQm8khTp06Rbdu3Wjbti3/+c9/rIifMZmMq0SRGyjkfBRO8DzuYcwtUVW++eYbfHx8mDt3Lm+88YYV8TMmE3I19HRcVUdlWCQmx9m7dy89e/akdu3aTJgwAV/fm+6Ua4zJBFz1KOwkdZPuYmNjWblyJQA+Pj4sX76cVatWWZIwJhNzlSiaZlgUJkf47bffaNy4MQ0bNowv4tegQQMbajImk0s2Uajq3xkZiMm+oqOjee+99wgMDGT79u1MnDjRivgZk4WkuiigMamhqjz00EOsWLGCdu3aMXbsWO655x5vh2WMSQV3aj0Zk2rXrl1DVRERevbsycyZM/nvf/9rScKYLMgShUl3a9eupVq1akyfPh2APn360LlzZyviZ0wWZYnCpJuLFy8yZMgQHnzwQS5dukTp0qW9HZIxJh3YHIVJF8uXL6d379788ccfDBw4kLfeeovChQt7OyxjTDqwRGHSxdmzZ8mXLx+rVq2iXr163g7HGJOOLFGYNJs7dy4nTpzg2WefpV27drRo0YLbbrvN22EZY9KZzVGYVDt58iSdO3emQ4cOTJkyJb6InyUJY7InSxTGbarKlClT8PHxITw8nH//+9+sXLnSrqw2JpuzoSfjtr1799K7d29q167NxIkTqVq1aobs9/r160RGRhIVFZUh+zMmK8ufPz9ly5Ylb9686bZNSxTGpdjYWFasWEHjxo3x8fFhxYoV1K1bl1y5Mq4zGhkZSeHChSlXrpxdi2GMC6rKmTNniIyM5IEHHki37drQk0nWvn37aNiwIU2aNIkv4levXr0MTRIAUVFRlChRwpKEMSkQEUqUKJHuvW9LFOYm169f5+233yYoKIiIiAgmT57s9SJ+liSMcY8n/q/Y0JNJRFVp2rQpq1atolOnTnz66afcdddd3g7LGONF1qMwAFy9ejW+iF/v3r2ZM2cOs2bNsiThlDt3bqpVq4a/vz+tW7fm7Nmz8esiIiJo0qQJVapUoVKlSrzxxhuoavz6H3/8kZCQEHx9fQkODuaFF17wwhGkTffu3QkMDOSjjz5yq32hQp65S7KqMnjwYCpWrEhgYGD8UOiNrly5QsOGDeNP2fa2lStXUr16dfLkycPs2bOTbbd582YCAgKoWLEigwcPjv/38/fff9OsWTMqVapEs2bN+OeffwCYP38+r732WoYcA+D4BWSlR40aNTStavQtqjX6Fk3z+7Or1atXa5UqVXTq1KneDiVJu3fv9nYIevvtt8c/79mzp7755puqqnr58mUtX768Ll68WFVVL126pGFhYfrZZ5+pqurOnTu1fPnyumfPHlVVjY6O1s8//zxdY7t+/Xq6bi/O8ePHtUKFCql6T8LPKT0tWLBAw8LCNDY2VteuXas1a9ZMst1nn32mH3/8sUdiSIvff/9dt2/fro8//rjOmjUr2XahoaG6du1ajY2N1bCwMF24cKGqqg4fPlzffvttVVV9++239cUXX1RV1djYWK1WrZpeunQpye0l9X8G2KRp/N61HkUOduHCBQYNGkT9+vWJiori7rvv9nZIKRoyBBo1St/HkCGpi6FOnTocPXoUgOnTp/Pggw/SvHlzAAoWLMhnn33GO++8A8B7773HK6+8En8qce7cuXn22Wdv2ubFixfp3bs3AQEBBAYGMmfOHCDxX+izZ8+mV69eAPTq1YtnnnmGWrVq8eKLL1KuXLlEvZxKlSpx8uRJTp8+TceOHQkNDSU0NJRff/31pn1HRUXF7zs4OJhly5YB0Lx5c44ePUq1atVYtWpVovecPHmS9u3bExQURFBQEGvWrLnpeJo2bUr16tUJCAhg3rx5AFy6dImWLVsSFBSEv78/3333HQAjRozA19eXwMBAhg0bdlOM8+bNo2fPnogItWvX5uzZsxw/fvymdtOmTaNt27YuY/jjjz/w9/ePf8/o0aMZOXIkAAcOHOChhx4iKCiI6tWrc/DgwZv2kRrlypUjMDDQ5Qkgx48f5/z589SuXTu+LP/3338ff9xPPPEEAE888UT86yJCo0aNmD9//i3F5y6PzlGISBjwCZAbmKCq79yw/nmgHxANnAb6qOqfnozJOCxdupTevXtz5MgRBg0axL///W+PDRtkJzExMfzyyy/07dsXcAw71ahRI1GbChUqcPHiRc6fP8+uXbvcGmp64403KFq0KDt37gSIH2JwJTIykjVr1pA7d25iYmKYO3cuvXv3Zv369dx///2ULl2aRx99lKFDh1KvXj0OHz7Mww8/zJ49exJtZ+zYsYgIO3fuZO/evTRv3pz9+/cTHh5Oq1at2LZt2037Hjx4MA0bNmTu3LnExMRw8eLFROvz58/P3LlzKVKkCH/99Re1a9emTZs2LFq0iHvuuYcFCxYAcO7cOc6cOcPcuXPZu3cvIpIo4cU5evQo9957b/xy2bJlOXr0aKI/bq5du8ahQ4coV66cyxhceeyxxxgxYgTt27cnKiqK2NjYm9rUr1+fCxcu3PT66NGjeeihh1xuPylHjx6lbNmyNx0bOBJy3DHeddddnDx5Mr5dSEgIq1atokuXLqneZ2p5LFGISG5gLNAMiAQ2iki4qu5O0GwrEKKql0XkWeA9oKunYjL/c/78eQoWLMjq1aupW7eut8Nx28cfe2e/V65coVq1ahw9ehQfHx+aNWuWrttfsmQJM2bMiF8uXrx4iu/p3Llz/FXxXbt2ZdSoUfTu3ZsZM2bQtWvX+O3u3v2//3Lnz5/n4sWLif4oWL16NYMGDQKgatWq3H///ezfv58iRYoku++lS5cyZcoUwNFLKlq0aKL1qsrLL7/MypUryZUrF0ePHuXkyZMEBATwwgsv8NJLL9GqVSvq169PdHQ0+fPnp2/fvrRq1YpWrVqleOxJ+euvvyhWrFiKMSTnwoULHD16lPbt2wOORJOUG3tXGUVEEp3RVKpUKY4dO5Yh+/bk0FNN4ICqHlLVa8AMoG3CBqq6TFUvOxfXAWUxHjN79mzGjh0LQLt27dixY0eWShLeVKBAAbZt28aff/6JqsZ/jr6+vmzevDlR20OHDlGoUCGKFCmCn5/fTetTI+EXw43nxt9+++3xz+vUqcOBAwc4ffo033//PR06dAAcF0yuW7eObdu2sW3bNo4ePZohPcdp06Zx+vRpNm/ezLZt2yhdujRRUVFUrlyZLVu2EBAQwKuvvsqoUaPIkycPGzZsoFOnTsyfP5+wsLCbtlemTBmOHDkSvxwZGUmZMmUStSlQoECizyi5GPLkyZOop5Daaw7q169PtWrVbnosWbIkVdtJeGyRkZFJHlvp0qXjh9iOHz9OqVKlEsVdoECBNO0ztTyZKMoARxIsRzpfS05f4MekVojIUyKySUQ2nT59Oh1DzBmOHz9Ohw4d6Ny5M9OnT48/IyQ9L/HPKQoWLMiYMWP44IMPiI6O5rHHHmP16tXxXxJXrlxh8ODBvPjiiwAMHz6ct956i/379wOOL+4vv/zypu02a9YsPvnA/4aeSpcuzZ49e4iNjWXu3LnJxiUitG/fnueffx4fHx9KlCgBOOYZPv300/h2SQ0j1a9fn2nTpgGwf/9+Dh8+TJUqVVx+Dk2bNuWLL74AHMNx586dS7T+3LlzlCpVirx587Js2TL+/NMxonzs2DEKFixIjx49GD58OFu2bOHixYucO3eOFi1a8NFHH7F9+/ab9temTRumTJmCqrJu3TqKFi1605xa8eLFiYmJif/iTy6G0qVLc+rUKc6cOcPVq1fjx/kLFy5M2bJl4+cBrl69yuXLl7nRqlWr4hNvwkdahp0A7r77booUKcK6devi66nFzbO0adOGr7/+GoCvv/46/nVw/K4SzrV4VFpnwVN6AJ1wzEvELT8OfJZM2x44ehT5UtqunfXkvtjYWJ00aZIWK1ZM8+fPr++++67HzpDxpMx21pOqaqtWrXTKlCmqqrpjxw5t2LChVq5cWStUqKAjR47U2NjY+LY//PCDVq9eXatWrao+Pj46fPjwm7Z/4cIF7dmzp/r5+WlgYKDOmTNHVVVnzZql5cuX11q1aumAAQP0iSeeUFXVJ5544qazaDZu3KiATp48Of6106dPa5cuXTQgIEB9fHz06aefvmnfV65c0V69eqm/v79Wq1ZNly5dqqqOM3b8/PyS/DxOnDihbdq0UX9/fw0KCtI1a9Yk+pxOnz6ttWvXVn9/f+3Vq5dWrVpVf//9d120aJEGBARoUFCQhoSE6MaNG/XYsWMaGhqqAQEB6u/vnyj+OLGxsdq/f38tX768+vv768aNG5OMq0+fPvrzzz+7jEFV9ZNPPtHy5ctr/fr19YknntDXX39dVVX379+vjRs31oCAAK1evboePHgwyf24a8OGDVqmTBktWLCg3nHHHerr6xu/LigoKP75xo0b1c/PT8uXL68DBgyI//fz119/aZMmTbRixYratGlTPXPmTPx7WrZsqTt27Ehyv+l91pMnE0UdYHGC5f8D/i+Jdg8Be4BS7mzXEoX7IiIiNFeuXFq/fn3dt2+ft8NJs8yQKEzWsHnzZu3Ro4e3w/C4EydOaJMmTZJdn5VOj90IVBKRB0TkNqAbEJ6wgYgEA+OANqp6yoOx5BgxMTEsXboUcIyfr169muXLl1O5cmUvR2aM51WvXp3GjRtnmgvuPOXw4cN88MEHGbY/jyUKVY0GBgKLcfQYZqpqhIiMEpG4c9TeBwoBs0Rkm4iEJ7M544Y9e/bQoEEDmjZtytatWwHHJGdGF/Ezxpv69OmT7e+REhoaSrVq1TJsfx69jkJVFwILb3jttQTP0zb7YxK5fv067733HqNGjaJQoUJMnTo1Q/8RGWOyNysKmMWpKk2aNGH16tV06dKFTz/9NNEpdMYYc6tsTCKLioqKQtVRxK9fv37MnTuX7777zpKEMSbdWaLIglauXElgYCDffPMN4KgB065dO+8GZYzJtixRZCHnz5+nf//+NGzYkOjo6ES1b4xnWZlx75YZ37t3L3Xq1CFfvnyMHj062XZxQ7Hnz5/3SByp5W7cv//+O7Vq1aJixYp07dqVa9euAY6L/rp27UrFihWpVasWf/zxBwA7d+6MLxCZESxRZBFLlizB39+fL7/8kqFDh7Jz504aNWrk7bByjLgSHrt27eKOO+6Iv4r6ypUrtGnThhEjRrBv3z62b9/OmjVr+PzzzwHYtWsXAwcO5JtvvmH37t1s2rSJihUrpmts0dHR6bq9OCdOnGDjxo3s2LGDoUOHemQf7rrjjjsYM2ZMkpVlE1q4cCFBQUEu61RlJHfjfumllxg6dCgHDhygePHiTJw4EYCJEydSvHhxDhw4wNChQ3nppZcACAgIIDIyksOHD3v8GMASRZZx+fJlChcuzJo1a/jwww8T1fnJUTJBnXErM57xZcZLlSpFaGhoimVnEpYZB0dNsxo1auDn58f48ePjX0/uc03puFLLnbhVlaVLl9KpUycgcTnxhGXGO3XqxC+//BLfW23dunWiQpKeZGc9ZVKqyqxZszh58iSDBg2iTZs2tGjRgjx57FfmTVZm3CGjy4y769dff2XcuHHxy5MmTeKOO+7gypUrhIaG0rFjx/g6WElJ6bjAUal33759N73+/PPP07Nnz1THfObMGYoVKxb/fzthmfGE5dXz5MlD0aJFOXPmDCVLliQkJIR33nknvq6YJ9m3TiZ07Ngx+vfvz7x583jwwQfp378/uXPntiQBXqszbmXGE8uMZcbBcevQwoULxy+PGTMmvpjikSNH+O2331wmipSOC4jvBXlbdikzblJJVZk4cSK+vr4sXryY999/n+XLl2f7q0yzAisznjrpXWbcXQlLiC9fvpwlS5awdu1atm/fTnBwcPxn6OpzTUnXrl2TLDMel2BSq0SJEpw9ezZ+rilhmfGE5dWjo6M5d+5cfKLLLmXGTSrt2bOHp556imrVqrFz506GDRtmvYhMxsqMO2R0mXF3ValShUOHDsXHULx4cQoWLMjevXtZt25dfLvkPteUjgscPYqkyoynZdgJHL+7xo0bM3v2bCBxOfGEZcZnz55NkyZN4pNctigz7qlHdqseGx0drT/99FP88tq1azUmJsaLEWU+maF6rJUZTyyjy4wfP35cy5Qpo4ULF9aiRYtqmTJl9Ny5cze1GzVqlH711VeqqhoVFaVhYWFatWpVbdu2rTZs2FCXLVvm8nNN7rjSylXcjzzyiB49elRVVQ8ePKihoaFaoUIF7dSpk0ZFRamq43fTqVMnrVChgoaGhiYqez5gwAANDw9Pcr/pXT1WNMH53llBSEiIbtq0KW3v7VcMgE0TzqZfQLcgIiKCvn37sn79erZs2UJwcLC3Q8qU9uzZg4+Pj7fDMFnA8ePH6dmzJz///LO3Q/Goq1ev0rBhQ1avXp3kqENS/2dEZLOqhqRlfzb05AXXrl1j1KhRBAcHc/DgQaZPn25F/IxJB3fffTdPPvlkprngzlMOHz7MO++8k2FD0zYAnsFUlUaNGrF27VoeffRRPv74Y+68805vh2VMttGlSxdvh+BxlSpVolKlShm2P+tRZJCERfyeeeYZwsPDmTZtmiUJY0ymZ4kiAyxfvhx/f3+mTp0KQM+ePWndurWXozLGGPdYovCgc+fO8fTTT9O4cWMA7r//fi9HZIwxqWeJwkN++ukn/Pz8mDBhAsOGDWPHjh00bNjQ22EZY0yqWaLwkKioKIoXL87atWt5//33KViwoLdDMrfAyox7t8z4tGnTCAwMJCAggLp16yZ7UZ5amXGPsESRTlSVb7/9lk8++QRwXFG5detWatas6eXITHqwMuPeLTP+wAMPsGLFCnbu3Mm//vUvnnrqqSTbWZlxD0nrlXreemTGK7OPHDmirVq1UkAbNGhgV1ans4RXmT7343Pa8D8N0/Xx3I/PpRhDwiuzv/jiC3322WdVVXXChAn6+OOPJ2p74MABLVu2rKqqPv744zpx4sQUt3/hwoX4q6MDAgJ09uzZN+131qxZia7Mfvrpp7VmzZo6dOhQvf/++/Wff/6Jb1uxYkU9ceKEnjp1Sjt06KAhISEaEhKiq1evvmnfyV2ZHRAQoPnz59egoCBduXJlovecOHFC27Vrp4GBgRoYGKi//vprongvXLigTZo00eDgYPX399fvv/9eVVUvXryoLVq00MDAQPXz89MZM2aoqupLL72kPj4+GhAQoC+88ILLz+rvv//We+65J8l13bt3j7/6WlW1bdu2Wr16dfX19dVx48bFv57c55rccd2q119/Xd9///0k18XGxmqJEiX0+vXrqqq6Zs0abd68uaqqNm/ePP7q8OvXr2uJEiXir/r/+OOP9d13301ym+l9ZbZdR3ELYmNj+eqrrxg+fDjR0dF8+OGHDB48mFy5rKOWXVmZcQdvlhmfOHEijzzySJLrrMy4Z1iiuAV79+6lf//+NGrUiK+++ory5ct7O6Rs7+Owj72yXysznpi3yowvW7aMiRMnsnr16iTXW5lxz7A/fVMpOjqaxYsXA44S0+vWrWPJkiWWJLI5KzOeOp4oM75jxw769evHvHnzkv2ytzLjnmGJIhV27txJ3bp1CQsLY+vWrQCEhoYm+kdnsjcrM+6Q0WXGDx8+TIcOHZg6dSqVK1dONi4rM+4haZ3c8NbDG5PZUVFR+tprr2mePHn0zjvv1BkzZiQqI208y8qMW5nxvn37arFixTQoKEiDgoI0ue8BKzPuYGXGM7jMuKpSt25d1q1bR48ePfjoo48oWbJkmvZv0sbKjBt3WZlxBysznkEuX74cX8RvwIABzJ8/n6lTp1qSMCYTszLjnmGJIgm//PIL/v7+8WODPXr0oGXLll6Oyhjjji5dumSaC+48pVKlSjRq1CjD9meJIoGzZ8/Sr18/HnroIfLkyUOFChW8HZJxympDpMZ4iyf+r1iicFq0aBG+vr5MnjyZl156ie3bt1O/fn1vh2VwXLx15swZSxbGpEBVOXPmDPnz50/X7doFd07Xr1+nVKlS/PDDDzddaWu8q2zZskRGRnL69Glvh2JMppc/f37Kli2brtvMsYlCVZk+fTqnTp1i6NChtG7dmhYtWsRf6Woyj7x58/LAAw94OwxjciyPDj2JSJiI7BORAyIyIon1+UTkO+f69SJSzpPxxDly5AitWrWiR48ezJs3L/5KTksSxhhzM48lChHJDYwFHgF8ge4i4ntDs77AP6paEfgIeNdT8YCjF3F6z1X8/PxYvnw5n3zyCb/88osV8TPGGBc8+Q1ZEzigqodU9RowA2h7Q5u2wNfO57OBpuLBehhRZ2M5vDaKWrVqsWvXLgYPHmy9CGOMSYEn5yjKAEcSLEcCtZJro6rRInIOKAH8lbCRiDwFxN2p5KKI3Fzj130llyxZ8lcOLuJXkhs+3xwmJx9/Tj52sON3XcDLhSwxma2q44Hx6bEtEdmU1svYswM7/px7/Dn52MGOX0TSVvsIzw49HQXuTbBc1vlakm1EJA9QFDjjwZiMMcakkicTxUagkog8ICK3Ad2A8BvahANPOJ93ApaqXVVljDGZiseGnpxzDgOBxUBuYJKqRojIKBzlbsOBicBUETkA/I0jmXhaugxhZWF2/DlXTj52sONP8/FnuTLjxhhjMpZdQGCMMcYlSxTGGGNcyraJIrOWD8kIbhz78yKyW0R2iMgvInK/N+L0lJSOP0G7jiKiIpKtTpl05/hFpIvz30CEiEzP6Bg9yY1///eJyDIR2er8P9DCG3F6gohMEpFTIrIrmfUiImOcn80OEanu1obTeg/VzPzAMXl+ECgP3AZsB3xvaNMf+NL5vBvwnbfjzsBjbwwUdD5/Nrscu7vH72xXGFgJrANCvB13Bv/+KwFbgeLO5VLejjuDj3888KzzuS/wh7fjTsfjbwBUB3Yls74F8CMgQG1gvTvbza49ikxXPiQDpXjsqrpMVS87F9fhuMYlu3Dndw/wBo7aYlEZGVwGcOf4nwTGquo/AKp6KoNj9CR3jl+BuFvgFQWOZWB8HqWqK3GcQZqctsAUdVgHFBORu1PabnZNFEmVDymTXBtVjQbiyodkde4ce0J9cfyFkV2kePzO7va9qrogIwPLIO78/isDlUXkVxFZJyJhGRad57lz/COBHiISCSwEBmVMaJlCar8fgCxSwsN4hoj0AEKAht6OJaOISC7gQ6CXl0Pxpjw4hp8a4ehNrhSRAFU9682gMlB3YLKqfiAidXBcy+WvqrHeDiyzyq49ipxcPsSdY0dEHgJeAdqo6tUMii0jpHT8hQF/YLmI/IFjnDY8G01ou/P7jwTCVfW6qv4O7MeROLIDd46/LzATQFXXAvlxFAzMCdz6frhRdk0UObl8SIrHLiLBwDgcSSI7jU9DCsevqudUtaSqllPVcjjmaNqoapoLpmUy7vzb/x5HbwIRKYljKOpQBsboSe4c/2GgKYCI+OBIFDnlPrvhQE/n2U+1gXOqejylN2XLoSfNvOVDPM7NY38fKATMcs7fH1bVNl4LOh25efzZlpvHvxhoLiK7gRhguKpmh960u8f/AvCViAzFMbHdK5v8kYiIfIvjj4CSzjmY14G8AKr6JY45mRbAAeAy0Nut7WaTz8cYY4yHZNehJ2OMMenEEoUxxhiXLFEYY4xxyRKFMcYYlyxRGGOMcckShcmURCRGRLYleJRz0fZiOuxvsoj87tzXFucVu6ndxgQR8XU+f/mGdWtuNUbnduI+l10i8oOIFEuhfbXsVB3VeIedHmsyJRG5qKqF0ruti21MBuar6mwRaQ6MVtXAW9jeLceU0nZF5Gtgv6r+20X7Xjiq4w5M71hMzmE9CpMliEgh570ztojIThG5qSKsiNwtIisT/MVd3/l6cxFZ63zvLBFJ6Qt8JVDR+d7nndvaJSJDnK/dLiILRGS78/WuzteXi0iIiLwDFHDGMc257qLz5wwRaZkg5ski0klEcovI+yKy0XmfgKfd+FjW4izoJiI1nce4VUTWiEgV55XJo4Cuzli6OmOfJCIbnG2TqqxrTGLerp9uD3sk9cBxxfA252MujioCRZzrSuK4sjSuR3zR+fMF4BXn89w46jqVxPHFf7vz9ZeA15LY32Sgk/N5Z2A9UAPYCdyO40r2CCAY6Ah8leC9RZ0/l+O8t0VcTAnaxMXYHvja+fw2HJU8CwBPAa86X88HbAIeSCLOiwmObxYQ5lwuAuRxPn8ImON83gv4LMH73wJ6OJ8Xw1Hn6XZv/77tkbkf2bKEh8kWrqhqtbgFEckLvCUiDYBYHH9JlwZOJHjPRmCSs+33qrpNRBriuDnNr85yJbfh+Es8Ke+LyKs46v70xVEPaK6qXnLG8F+gPrAI+EBE3sUxXLUqFcf1I/CJiOQDwoCVqnrFOdwVKCKdnO2K4ijU9/sN7y8gItucx78H+DlB+69FpBKOshR5k9l/c6CNiAxzLucH7nNuy5gkWaIwWcVjwJ1ADVW9Lo7Kr/kTNlDVlc5E0hKYLCIfAv8AP6tqdzf2MVxVZ8ctiEjTpBqp6n5x3NOiBfCmiPyiqqPcOQhVjRKR5cDDQFccN9YBxx3HBqnq4hQ2cUVVq4lIQRz1jAYAY3DciGmZqrZ3TvwvT+b9AnRU1X3uxGsM2ByFyTqKAqecSaIxcNN9vsVx7++TqvoVMAHHLSHXAQ+KSNycw+0iUtnNfa4C2olIQRG5Hcew0SoRuQe4rKrf4CiwmNR9h687ezZJ+Q5HMba43gk4vvSfjXuPiFR27jNJ6rhD4WDgBflfmfy4ctG9EjS9gGMILs5iYJA4u1fiqCRsjEuWKExWMQ0IEZGdQE9gbxJtGgHbRWQrjr/WP1HV0zi+OL8VkR04hp2qurNDVd2CY+5iA445iwmquhUIADY4h4BeB95M4u3jgR1xk9k3+AnHzaKWqON2neBIbLuBLSKyC0cZeJc9fmcsO3DciOc94G3nsSd83zLAN24yG0fPI68ztgjnsjEu2emxxhhjXLIehTHGGJcsURhjjHHJEoUxxhiXLFEYY4xxyRKFMcYYlyxRGGOMcckShTHGGJf+P2YSHGvZNVYiAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Other Checks That Weren't Displayed

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Check
Trust Score ComparisonDeepchecksValueError: Number of samples in test dataset have not passed the minimum. you can change minimum samples needed to run with parameter \"min_test_samples\"Trust Score ComparisonDeepchecksValueError: Number of samples in test dataset have not passed the minimum. you can change minimum samples needed to run with parameter \"min_test_samples\"
Index Train Test LeakageDeepchecksValueError: Check IndexTrainTestLeakage requires dataset to have an index columnIndex Train Test LeakageDeepchecksValueError: Check IndexTrainTestLeakage requires dataset to have an index column
Date Train Test Leakage DuplicatesDeepchecksValueError: Check DateTrainTestLeakageDuplicates requires dataset to have a date columnDate Train Test Leakage DuplicatesDeepchecksValueError: Check DateTrainTestLeakageDuplicates requires dataset to have a date column
Date Train Test Leakage OverlapDeepchecksValueError: Check DateTrainTestLeakageOverlap requires dataset to have a date columnDate Train Test Leakage OverlapDeepchecksValueError: Check DateTrainTestLeakageOverlap requires dataset to have a date column
Boosting OverfitDeepchecksValueError: Unsupported model of type: RandomForestClassifierBoosting OverfitDeepchecksValueError: Unsupported model of type: RandomForestClassifier
Trust Score ComparisonDeepchecksValueError: Number of samples in test dataset have not passed the minimum. you can change minimum samples needed to run with parameter \"min_test_samples\"Trust Score ComparisonDeepchecksValueError: Number of samples in test dataset have not passed the minimum. you can change minimum samples needed to run with parameter \"min_test_samples\"
Category Mismatch Train TestNothing foundCategory Mismatch Train TestNothing found
String Mismatch ComparisonNothing foundString Mismatch ComparisonNothing found
Label Ambiguity - Test DatasetNothing foundLabel Ambiguity - Test DatasetNothing found
Label Ambiguity - Train DatasetNothing foundLabel Ambiguity - Train DatasetNothing found
Special Characters - Test DatasetNothing foundSpecial Characters - Test DatasetNothing found
Special Characters - Train DatasetNothing foundSpecial Characters - Train DatasetNothing found
String Length Out Of Bounds - Test DatasetNothing foundString Length Out Of Bounds - Test DatasetNothing found
String Length Out Of Bounds - Train DatasetNothing foundString Length Out Of Bounds - Train DatasetNothing found
Rare Format Detection - Test DatasetNothing foundRare Format Detection - Test DatasetNothing found
Rare Format Detection - Train DatasetNothing foundRare Format Detection - Train DatasetNothing found
String Mismatch - Test DatasetNothing foundString Mismatch - Test DatasetNothing found
Data Duplicates - Train DatasetNothing foundData Duplicates - Train DatasetNothing found
Dominant Frequency ChangeNothing foundDominant Frequency ChangeNothing found
String Mismatch - Train DatasetNothing foundString Mismatch - Train DatasetNothing found
Mixed Types - Test DatasetNothing foundMixed Types - Test DatasetNothing found
Mixed Types - Train DatasetNothing foundMixed Types - Train DatasetNothing found
Mixed Nulls - Test DatasetNothing foundMixed Nulls - Test DatasetNothing found
Mixed Nulls - Train DatasetNothing foundMixed Nulls - Train DatasetNothing found
Single Value in Column - Test DatasetNothing foundSingle Value in Column - Test DatasetNothing found
Single Value in Column - Train DatasetNothing foundSingle Value in Column - Train DatasetNothing found
Data Duplicates - Test DatasetNothing foundData Duplicates - Test DatasetNothing found
New Label Train TestNothing foundNew Label Train TestNothing found
\n", @@ -1787,94 +1875,98 @@ "data": { "text/plain": [ "Overall Suite: [\n", - "\t0: TrainTestDrift(max_num_categories=10, sort_feature_by=feature importance, n_top_columns=5)\n", + "\t0: TrainTestDrift(run=, max_num_categories=10, sort_feature_by=feature importance, n_top_columns=5)\n", "\t\tConditions:\n", "\t\t\t0: PSI and Earth Mover's Distance cannot be greater than 0.2 and 0.1 respectively\n", - "\t1: TrustScoreComparison(k_filter=10, alpha=0.001, max_number_categories=10, min_test_samples=300, sample_size=10000, random_state=42, n_to_show=5, percent_top_scores_to_hide=0.01)\n", + "\t1: TrustScoreComparison(run=, k_filter=10, alpha=0.001, max_number_categories=10, min_test_samples=300, sample_size=10000, random_state=42, n_to_show=5, percent_top_scores_to_hide=0.01)\n", "\t\tConditions:\n", "\t\t\t0: Mean trust score decline is not greater than 20.00%\n", - "\t2: IndexTrainTestLeakage(n_index_to_show=5)\n", + "\t2: IndexTrainTestLeakage(run=, n_index_to_show=5)\n", "\t\tConditions:\n", "\t\t\t0: Index leakage is not greater than 0%\n", - "\t3: DateTrainTestLeakageDuplicates(n_to_show=5)\n", + "\t3: DateTrainTestLeakageDuplicates(run=, n_to_show=5)\n", "\t\tConditions:\n", "\t\t\t0: Date leakage ratio is not greater than 0%\n", - "\t4: DateTrainTestLeakageOverlap\n", + "\t4: DateTrainTestLeakageOverlap(run=)\n", "\t\tConditions:\n", "\t\t\t0: Date leakage ratio is not greater than 0%\n", - "\t5: TrainTestSamplesMix\n", + "\t5: TrainTestSamplesMix(run=)\n", "\t\tConditions:\n", "\t\t\t0: Percentage of test data samples that appear in train data not greater than 10.00%\n", - "\t6: SingleFeatureContribution(n_show_top=5)\n", + "\t6: SingleFeatureContribution(run=, n_show_top=5)\n", "\t\tConditions:\n", "\t\t\t0: Features' Predictive Power Score (PPS) is not greater than 0.8\n", - "\t7: SingleFeatureContributionTrainTest(n_show_top=5)\n", + "\t7: SingleFeatureContributionTrainTest(run=, n_show_top=5)\n", "\t\tConditions:\n", "\t\t\t0: Train-Test features' Predictive Power Score (PPS) difference is not greater than 0.2\n", - "\t8: TrainTestDifferenceOverfit\n", + "\t8: TrainTestDifferenceOverfit(run=)\n", "\t\tConditions:\n", - "\t\t\t0: Train-Test metrics degradation percentage is not greater than 0.2\n", - "\t9: BoostingOverfit(num_steps=20)\n", + "\t\t\t0: Train-Test metrics degradation ratio is not greater than 0.1\n", + "\t9: BoostingOverfit(run=, num_steps=20)\n", "\t\tConditions:\n", "\t\t\t0: Test score decline is not greater than 5.00%\n", - "\t10: UnusedFeatures(feature_importance_threshold=0.2, feature_variance_threshold=0.4, n_top_fi_to_show=5, n_top_unused_to_show=15, random_state=42)\n", + "\t10: UnusedFeatures(run=, feature_importance_threshold=0.2, feature_variance_threshold=0.4, n_top_fi_to_show=5, n_top_unused_to_show=15, random_state=42)\n", "\t\tConditions:\n", "\t\t\t0: Number of high variance unused features is not greater than 5\n", - "\t11: ModelInferenceTimeCheck(number_of_samples=1000)\n", + "\t11: ModelInferenceTimeCheck(number_of_samples=1000, run=)\n", "\t\tConditions:\n", "\t\t\t0: Average model inference time for one sample is not greater than 0.001\n", - "\t12: PerformanceReport\n", - "\t13: SimpleModelComparison(simple_model_type=constant, maximum_ratio=50, max_depth=3, random_state=42)\n", + "\t12: DatasetsSizeComparison(run=)\n", + "\t\tConditions:\n", + "\t\t\t0: Train dataset is not smaller than test dataset.\n", + "\t\t\t1: Test-Train size ratio is not smaller than 0.01.\n", + "\t13: PerformanceReport(run=)\n", + "\t14: SimpleModelComparison(run=, simple_model_type=constant, maximum_ratio=50, max_depth=3, random_state=42)\n", "\t\tConditions:\n", "\t\t\t0: Ratio not less than 1.1 between the given model's result and the simple model's result\n", - "\t14: ConfusionMatrixReport\n", - "\t15: RocReport(excluded_classes=[])\n", + "\t15: ConfusionMatrixReport(run=)\n", + "\t16: RocReport(run=, excluded_classes=[])\n", "\t\tConditions:\n", "\t\t\t0: Not less than 0.7 AUC score for all the classes\n", - "\t16: CalibrationMetric\n", - "\t17: TrustScoreComparison(k_filter=10, alpha=0.001, max_number_categories=10, min_test_samples=300, sample_size=10000, random_state=42, n_to_show=5, percent_top_scores_to_hide=0.01)\n", + "\t17: CalibrationMetric(run=)\n", + "\t18: TrustScoreComparison(run=, k_filter=10, alpha=0.001, max_number_categories=10, min_test_samples=300, sample_size=10000, random_state=42, n_to_show=5, percent_top_scores_to_hide=0.01)\n", "\t\tConditions:\n", "\t\t\t0: Mean trust score decline is not greater than 20.00%\n", - "\t18: ClassPerformanceImbalance\n", + "\t19: ClassPerformanceImbalance(run=)\n", "\t\tConditions:\n", "\t\t\t0: Relative ratio difference between labels 'F1' score is not greater than 30.00%\n", - "\t19: IsSingleValue\n", + "\t20: IsSingleValue(run=)\n", "\t\tConditions:\n", "\t\t\t0: Does not contain only a single value for all columns\n", - "\t20: MixedNulls(check_nan=True, n_top_columns=10)\n", + "\t21: MixedNulls(run=, check_nan=True, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: Not more than 1 different null types for all columns\n", - "\t21: MixedTypes(n_top_columns=10)\n", + "\t22: MixedTypes(run=, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: Rare type ratio is not less than 1.00% of samples in all columns\n", - "\t22: StringMismatch(n_top_columns=10)\n", + "\t23: StringMismatch(run=, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: No string variants for all columns\n", - "\t23: DataDuplicates(n_to_show=5)\n", + "\t24: DataDuplicates(run=, n_to_show=5)\n", "\t\tConditions:\n", "\t\t\t0: Duplicate data is not greater than 0%\n", - "\t24: RareFormatDetection(patterns=[Pattern(digits and letters format (case sensitive)), Pattern(digits and letters format), Pattern(digits only format (ignoring letters)), Pattern(letters only format (ignoring digits)), Pattern(digits or letters format), Pattern(sequences of digits only format (ignoring letters)), Pattern(sequences of letters only format (ignoring letters)), Pattern(any sequence format)], rarity_threshold=0.05, min_unique_common_ratio=0.01, pattern_match_method=first, n_top_columns=10)\n", + "\t25: RareFormatDetection(run=, patterns=[Pattern(digits and letters format (case sensitive)), Pattern(digits and letters format), Pattern(digits only format (ignoring letters)), Pattern(letters only format (ignoring digits)), Pattern(digits or letters format), Pattern(sequences of digits only format (ignoring letters)), Pattern(sequences of letters only format (ignoring letters)), Pattern(any sequence format)], rarity_threshold=0.05, min_unique_common_ratio=0.01, pattern_match_method=first, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: Rare formats ratio is not greater than 0\n", - "\t25: StringLengthOutOfBounds(num_percentiles=1000, inner_quantile_range=94, outlier_factor=4, n_top_columns=10)\n", + "\t26: StringLengthOutOfBounds(run=, num_percentiles=1000, inner_quantile_range=94, outlier_factor=4, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: Ratio of outliers not greater than 0% string length outliers for all columns\n", - "\t26: SpecialCharacters(n_most_common=2, n_top_columns=10)\n", + "\t27: SpecialCharacters(run=, n_most_common=2, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: Ratio of entirely special character samples not greater than 0.10% for all columns\n", - "\t27: LabelAmbiguity(n_to_show=5)\n", + "\t28: LabelAmbiguity(run=, n_to_show=5)\n", "\t\tConditions:\n", "\t\t\t0: Ambiguous sample ratio is not greater than 0%\n", - "\t28: StringMismatchComparison(n_top_columns=10)\n", + "\t29: StringMismatchComparison(run=, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: No new variants allowed in test data for all columns\n", - "\t29: CategoryMismatchTrainTest\n", + "\t30: CategoryMismatchTrainTest(run=)\n", "\t\tConditions:\n", "\t\t\t0: Number of new category values is not greater than 0 for all columns\n", - "\t30: DominantFrequencyChange(dominance_ratio=2, ratio_change_thres=1.5, n_top_columns=10)\n", + "\t31: DominantFrequencyChange(run=, dominance_ratio=2, ratio_change_thres=1.5, n_top_columns=10)\n", "\t\tConditions:\n", "\t\t\t0: Change in ratio of dominant value in data not more than 25.00%\n", - "\t31: NewLabelTrainTest\n", + "\t32: NewLabelTrainTest(run=)\n", "\t\tConditions:\n", "\t\t\t0: Number of new label values is not greater than 0\n", "]" @@ -1904,7 +1996,7 @@ { "data": { "text/plain": [ - "DateTrainTestLeakageOverlap\n", + "DateTrainTestLeakageOverlap(run=)\n", "\tConditions:\n", "\t\t0: Date leakage ratio is not greater than 0%" ] @@ -1948,7 +2040,7 @@ { "data": { "text/plain": [ - "DateTrainTestLeakageOverlap" + "DateTrainTestLeakageOverlap(run=)" ] }, "execution_count": 13, @@ -2000,11 +2092,18 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Overall Suite: 0%| | 0/33 [00:00\n", - "#T_5ab36_ table {\n", + "#T_3ff04_ table {\n", " text-align: left;\n", "}\n", - "#T_5ab36_ thead {\n", + "#T_3ff04_ thead {\n", " text-align: left;\n", "}\n", - "#T_5ab36_ tbody {\n", + "#T_3ff04_ tbody {\n", " text-align: left;\n", "}\n", - "#T_5ab36_ th {\n", + "#T_3ff04_ th {\n", " text-align: left;\n", "}\n", - "#T_5ab36_ td {\n", + "#T_3ff04_ td {\n", " text-align: left;\n", "}\n", "\n", - "\n", + "
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Status
✖
Train Test DriftPSI and Earth Mover's Distance cannot be greater than 0.2 and 0.1 respectivelyFound numeric columns with Earth Mover's Distance over 0.1: sepal length (cm), petal length (cm), petal width (cm)
✖
Train Test DriftPSI and Earth Mover's Distance cannot be greater than 0.2 and 0.1 respectivelyFound numeric columns with Earth Mover's Distance over 0.1: sepal length (cm), petal length (cm), petal width (cm)
✖
Data Duplicates - Test DatasetDuplicate data is not greater than 0%Found 2.00% duplicate data
✖
Data Duplicates - Test DatasetDuplicate data is not greater than 0%Found 2.00% duplicate data
✖
Single Value in Column - Test DatasetDoes not contain only a single value for all columnsColumns containing a single value: ['target']
✖
Single Value in Column - Test DatasetDoes not contain only a single value for all columnsColumns containing a single value: ['target']
✖
Train-Test Difference OverfitTrain-Test metrics degradation percentage is not greater than 0.2Found degraded metrics: {'Precision': '100%', 'Recall': '100%'}
✖
Train-Test Difference OverfitTrain-Test metrics degradation ratio is not greater than 0.1Found performance degradation in: Precision (train=100% test=0%), Recall (train=100% test=0%)
✖
New Label Train TestNumber of new label values is not greater than 0Found 1 new labels: [2]
✖
New Label Train TestNumber of new label values is not greater than 0Found 1 new labels: [2]
✖
Single Feature Contribution - Train DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8Features with PPS above threshold: petal length (cm), petal width (cm)
✖
Single Feature Contribution - Train DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8Features with PPS above threshold: petal length (cm), petal width (cm)
✖
Single Feature Contribution Train-TestTrain-Test features' Predictive Power Score (PPS) difference is not greater than 0.2Features with PPS difference above threshold: petal length (cm), petal width (cm), sepal length (cm), sepal width (cm)
✖
Single Feature Contribution Train-TestTrain-Test features' Predictive Power Score (PPS) difference is not greater than 0.2Features with PPS difference above threshold: petal length (cm), petal width (cm), sepal length (cm), sepal width (cm)
✓
Single Feature Contribution - Test DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8
✓
Single Feature Contribution - Test DatasetFeatures' Predictive Power Score (PPS) is not greater than 0.8
✓
Category Mismatch Train TestNumber of new category values is not greater than 0 for all columns
✓
Category Mismatch Train TestNumber of new category values is not greater than 0 for all columns
✓
String Mismatch ComparisonNo new variants allowed in test data for all columns
✓
String Mismatch ComparisonNo new variants allowed in test data for all columns
✓
Label Ambiguity - Test DatasetAmbiguous sample ratio is not greater than 0%
✓
Label Ambiguity - Test DatasetAmbiguous sample ratio is not greater than 0%
✓
Label Ambiguity - Train DatasetAmbiguous sample ratio is not greater than 0%
✓
Label Ambiguity - Train DatasetAmbiguous sample ratio is not greater than 0%
✓
Special Characters - Test DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
Special Characters - Test DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
Special Characters - Train DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
Special Characters - Train DatasetRatio of entirely special character samples not greater than 0.10% for all columns
✓
String Length Out Of Bounds - Test DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
String Length Out Of Bounds - Test DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
String Length Out Of Bounds - Train DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
String Length Out Of Bounds - Train DatasetRatio of outliers not greater than 0% string length outliers for all columns
✓
Rare Format Detection - Test DatasetRare formats ratio is not greater than 0
✓
Rare Format Detection - Test DatasetRare formats ratio is not greater than 0
✓
Rare Format Detection - Train DatasetRare formats ratio is not greater than 0
✓
Rare Format Detection - Train DatasetRare formats ratio is not greater than 0
✓
Train Test Samples MixPercentage of test data samples that appear in train data not greater than 10.00%
✓
Train Test Samples MixPercentage of test data samples that appear in train data not greater than 10.00%
✓
Data Duplicates - Train DatasetDuplicate data is not greater than 0%
✓
Data Duplicates - Train DatasetDuplicate data is not greater than 0%
✓
String Mismatch - Test DatasetNo string variants for all columns
✓
String Mismatch - Test DatasetNo string variants for all columns
✓
Mixed Types - Test DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
String Mismatch - Train DatasetNo string variants for all columns
✓
Dominant Frequency ChangeChange in ratio of dominant value in data not more than 25.00%
✓
Mixed Types - Train DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
Mixed Nulls - Test DatasetNot more than 1 different null types for all columns
✓
Dominant Frequency ChangeChange in ratio of dominant value in data not more than 25.00%
✓
Mixed Nulls - Train DatasetNot more than 1 different null types for all columns
✓
Mixed Nulls - Train DatasetNot more than 1 different null types for all columns
✓
Single Value in Column - Train DatasetDoes not contain only a single value for all columns
✓
Single Value in Column - Train DatasetDoes not contain only a single value for all columns
✓
Class Performance Imbalance - Train DatasetRelative ratio difference between labels 'F1' score is not greater than 30.00%
✓
Class Performance Imbalance - Train DatasetRelative ratio difference between labels 'F1' score is not greater than 30.00%
✓
ROC Report - Train DatasetNot less than 0.7 AUC score for all the classes
✓
ROC Report - Train DatasetNot less than 0.7 AUC score for all the classes
✓
Simple Model ComparisonRatio not less than 1.1 between the given model's result and the simple model's result
✓
Simple Model ComparisonRatio not less than 1.1 between the given model's result and the simple model's result
✓
Model Inference Time Check - Test DatasetAverage model inference time for one sample is not greater than 0.001
✓
Datasets Size ComparisonTest-Train size ratio is not smaller than 0.01.
✓
Model Inference Time Check - Train DatasetAverage model inference time for one sample is not greater than 0.001
✓
Datasets Size ComparisonTrain dataset is not smaller than test dataset.
✓
Unused FeaturesNumber of high variance unused features is not greater than 5
✓
Model Inference Time Check - Test DatasetAverage model inference time for one sample is not greater than 0.001
✓
String Mismatch - Train DatasetNo string variants for all columns
✓
Model Inference Time Check - Train DatasetAverage model inference time for one sample is not greater than 0.001
✓
Mixed Types - Train DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
Unused FeaturesNumber of high variance unused features is not greater than 5
✓
Mixed Types - Test DatasetRare type ratio is not less than 1.00% of samples in all columns
✓
Mixed Nulls - Test DatasetNot more than 1 different null types for all columns
\n" @@ -2309,7 +2420,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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8Lm5rkrhlq+pzLb0OVX1WVb/mnk+BOxJ9HSKSC5yI01rX3Ou8V1UPB0bjdDFd21wMbvfkIzjdXANUtS+wkC//PpqzAegvIn1b2NdWfRiTcpbIGNMMEckXkVOB54FnVHVBB4vaCgyQuMtomznXHe5ATZ+I5AFXAitVtQRnvMOJInKOu3+AiIyN+8C9TUTy3A+rX/Pl8SAAqGoM58PsnriBq8NE5OQOvq625OF8k68TkSNxWkhasw2I4YxbSYbLgONVtTp+Y4L19izOIO8L+KJbCZz6u8JtrRERyRFnUHNecwGIyH4icrw7ZqQOJ5GMtRW4OIOODwdeA0qB/zRzzBFuHH6c8Uh1cWVvZdd6zMFJbra5z/0RTotMm9zuqneAB0Skn4j4RaRxPFO76sOYVLFExphdvSEilTjfNm/AmRPmR60/pWWquhRnfMhqt/m9uWb3bJzuizKcwbl74Fyxgqqux2lZ+A1OF0MhcIj7vKtwPsRW47QYPYsz+LIlvwNWAtNFpAL4ENivo6/N1XiVUePt1+72nwG3unV5E07y0CK3O+M2nMuNy0SkU+MsVHWVqs5qYXer9aaqn7v7d8P5EG/cPgv4CU6XTilOXV7SShhBnAHW23G6bgYBv2/l+Ovc+irB6RKbDXy1aTLmysdJJEpxusZKgDvdfY/hjMspE5HXVHUxzjigaThJzsHAp63E0dRFOK1YS4FinO6yjtSHMSkhqklr0TXGGGOM6VLWImOMMcaYjGWJjDHGGGMyliUyxhhjjMlYlsgYY4wxJmOlaiG5lBk4cKCOHDky3WEYY4wxpovMnj17u6o2O4FnxiUyJf37sOO236U7jF6jvO9P0h1Cr5I759x0h9Cr6KxkrDBheiPPY0+mO4ReRUTWtbTPupaMMcYYk7EskTHGGGNMxsqIREZELheRWSIyi/KKdIdjjDHGmG4iIxIZVX1YVcep6jj6tLjAqzHGGGN6mYxIZIwxxhhjmmOJjDHGGGMyliUyxhhjjMlYGZHI2GBfY4wxxjQnIxIZG+xrjDHGmOZkRCJjjDHGGNMcS2SMMcYYk7EskTHGGGNMxrJExhhjjDEZKyMSGbtqyRhjjDHNyYhExq5aMsYYY0xzMiKRMcYYY4xpjqhqumNol3HjxumsWbPSHYYxxhhjuoiIzFbVcc3uy7RERkQqgWXpjqMXGQhsT3cQvYjVd9ey+u5aVt9dqyfV9x6qWtDcDl9XR5IEy1rKykzyicgsq++uY/Xdtay+u5bVd9fqLfVtY2SMMcYYk7EskTHGGGNMxsrERObhdAfQy1h9dy2r765l9d21rL67Vq+o74wb7GuMMcYY0ygTW2SMMcYYYwBLZIwxxhiTwbptIiMi3xKRZSKyUkSub2Z/UERecPd/LiIj0xBmj5FAfX9DROaISEREzk5HjD1JAvX9axFZLCLzReQjEdkjHXH2FAnU9xUiskBECkVkqoiMTkecPUVb9R133FkioiLS4y8RTpUE3tuXiMg2971dKCI/TkecKaWq3e4GeIFVwJ5AAJgHjG5yzM+AB9375wEvpDvuTL0lWN8jgTHAU8DZ6Y45k28J1vdxQLZ7/0p7f6e8vvPj7p8OvJvuuDP1lkh9u8flAZ8A04Fx6Y47E28JvrcvAf6V7lhTeeuuLTJHAitVdbWqNgDPA99tcsx3gSfd+y8DJ4iIdGGMPUmb9a2qa1V1PhBLR4A9TCL1PVFVa9yH04HhXRxjT5JIfVfEPcwB7CqIjkvk7zfAn4E7gLquDK6HSbSue7TumsgMAzbEPS5ytzV7jKpGgHJgQJdE1/MkUt8medpb35cB76Q0op4tofoWkZ+LyCrg78DVXRRbT9RmfYvIYcDuqvpWVwbWAyX6t+Qst5v6ZRHZvWtC6zrdNZExxgAiciEwDrgz3bH0dKp6v6ruBfwO+GO64+mpRMQD3A38Jt2x9BJvACNVdQzwAV/0ZPQY3TWR2QjEZ43D3W3NHiMiPqAPUNIl0fU8idS3SZ6E6ltETgRuAE5X1fouiq0nau/7+3ngjFQG1MO1Vd95wEHAJBFZC3wFmGADfjukzfe2qpbE/f14FDi8i2LrMt01kZkJ7CMio0QkgDOYd0KTYyYAF7v3zwY+Vndkk2m3ROrbJE+b9S0ihwIP4SQxxWmIsSdJpL73iXv4HWBFF8bX07Ra36parqoDVXWkqo7EGQN2uqrOSk+4GS2R9/bQuIenA0u6ML4u0S1Xv1bViIj8AngPZ1T246q6SERuBWap6gTgMeBpEVkJ7MD5BZoOSKS+ReQI4FWgH3CaiPxJVQ9MY9gZK8H3951ALvCSO4Z9vaqenragM1iC9f0LtwUsDJTyxZck004J1rdJggTr+moROR2I4HxWXpK2gFPEligwxhhjTMbqrl1LxhhjjDFtskTGGGOMMRnLEhljjDHGZCxLZIwxxhiTsSyRMcYYY0zGskTGGGOMMRnLEhljjDHGZCxLZIwxxhiTsSyRMcYYY0zGskTGGGOMMRnLEhljjDHGZCxLZIwxxhiTsSyRMcYYY0zGskTGGNNpIvKgiNyYpLJGiEiViHjdx5NE5MfJKNst7x0RuThZ5Rlj0suX7gCMMd2fiKwFBgMRIAosBp4CHlbVmKpe0Y5yfqyqH7Z0jKquB3I7G7N7vluAvVX1wrjyT0lG2caY7sFaZIwxiTpNVfOAPYDbgd8BjyXzBCJiX66MMe1iiYwxpl1UtVxVJwDnAheLyEEi8oSI/AVARAaKyJsiUiYiO0Rkioh4RORpYATwhtt1dJ2IjBQRFZHLRGQ98HHctvikZi8RmSEiFSLyuoj0d891rIgUxccnImtF5EQR+RbwB+Bc93zz3P07u6rcuP4oIutEpFhEnhKRPu6+xjguFpH1IrJdRG5Ibe0aY9rLEhljTIeo6gygCPh6k12/cbcX4HRH/cE5XC8C1uO07OSq6t/jnnMMcABwcgun+yFwKTAUp3vr3gTiexf4K/CCe75DmjnsEvd2HLAnTpfWv5oc8zVgP+AE4CYROaCtcxtjuo4lMsaYztgE9G+yLYyTcOyhqmFVnaKq2kY5t6hqtarWtrD/aVVdqKrVwI3AOY2DgTvpAuBuVV2tqlXA74HzmrQG/UlVa1V1HjAPaC4hMsakiSUyxpjOGAbsaLLtTmAl8L6IrBaR6xMoZ0M79q8D/MDAhKNs2W5uefFl+3BakhptibtfQ5IGIhtjksMSGWNMh4jIETiJzNT47apaqaq/UdU9gdOBX4vICY27WyiurRab3ePuj8Bp9dkOVAPZcTF5cbq0Ei13E87g5fiyI8DWNp5njOkmLJExxrSLiOSLyKnA88Azqrqgyf5TRWRvERGgHOdy7Zi7eyvOWJT2ulBERotINnAr8LKqRoHlQEhEviMifuCPQDDueVuBkSLS0t+654BficgoEcnlizE1kQ7EaIxJA0tkjDGJekNEKnG6eW4A7gZ+1Mxx+wAfAlXANOABVZ3o7vsb8Ef3iqbftuPcTwNP4HTzhICrwbmCCvgZ8CiwEaeFJv4qppfcnyUiMqeZch93y/4EWAPUAVe1Iy5jTJpJ22PwjDHGGGO6J2uRMcYYY0zGskTGGGOMMRnLEhljjDHGZCxLZIwxxhiTsTJugbaBAwfqyJEj0x2GMcYYY7rI7Nmzt6tqQXP7Mi6RGTlyJLNmzUp3GMYYY4zpIiKyrqV9Ke1aEpFvicgyEVnZ3DTlIjJCRCaKyFwRmS8i305lPMYYY4zpWVKWyLhThd8PnAKMBs4XkdFNDvsj8KKqHgqcBzyQqniMMcYY0/OkskXmSGClu6psA8505t9tcowC+e79PjjrnhhjjDHGJCSVY2SGseuKtUXA+CbH3IKzQu5VQA5wYnMFicjlwOUAI0aMSHqgxhhjeqdwOExRURF1dXXpDsUAoVCI4cOH4/f7E35Ougf7ng88oap3ichRwNMicpCqxuIPUtWHgYcBxo0bZ2sqGGOMSYqioiLy8vIYOXIkzjqnJl1UlZKSEoqKihg1alTCz0tl19JGYPe4x8PdbfEuA14EUNVpOIvBDUxhTMYYY8xOdXV1DBgwwJKYbkBEGDBgQLtbx1KZyMwE9hGRUSISwBnMO6HJMeuBEwBE5ACcRGZbCmMyxhhjdmFJTPfRkd9FyhIZVY0AvwDeA5bgXJ20SERuFZHT3cN+A/xEROYBzwGXqC3HbYwxxpgEpXSMjKq+DbzdZNtNcfcXA0enMgZjjDEmUWee+Rpr11YkrbyRI/P53//OaHF/SUkJJ5xwAgBbtmzB6/VSUOBMYDtjxgwCgUCLz501axZPPfUU9957b4die+KJJ5g1axb/+te/Wjxm0qRJBAIBvvrVr3boHM0pKyvj2Wef5Wc/+1lSykv3YF9jjDGm21i7toJhw3KTWl5rBgwYQGFhIQC33HILubm5/Pa3v925PxKJ4PM1/1E9btw4xo0bl7RYmzNp0iRyc3OTnsg88MADSUtkbNFIY4wxAMQ0xvQNq4jEoukOpVe75JJLuOKKKxg/fjzXXXcdM2bM4KijjuLQQw/lq1/9KsuWLQOcJOPUU08FnCTo0ksv5dhjj2XPPfdssZXmP//5D/vuuy9HHnkkn3766c7tb7zxBuPHj+fQQw/lxBNPZOvWraxdu5YHH3yQe+65h7FjxzJlypRmjwOYPHkyY8eOZezYsRx66KFUVlYCcOedd3LEEUcwZswYbr75ZgCuv/56Vq1axdixY7n22ms7XV/WImOMMQaAZdu38N7KhYT8fsYOsTm70qmoqIjPPvsMr9dLRUUFU6ZMwefz8eGHH/KHP/yBV1555UvPWbp0KRMnTqSyspL99tuPK6+8cpf5WDZv3szNN9/M7Nmz6dOnD8cddxyHHnooAF/72teYPn06IsKjjz7K3//+d+666y6uuOKKXVqJSktLmz3uH//4B/fffz9HH300VVVVhEIh3n//fVasWMGMGTNQVU4//XQ++eQTbr/9dhYuXLizJaqzLJExxhhDNBZj0tplBH0+Plm7nIMGDcPn8aY7rF7r+9//Pl6vU//l5eVcfPHFrFixAhEhHA43+5zvfOc7BINBgsEggwYNYuvWrQwfPnzn/s8//5xjjz125xicc889l+XLlwNO4nTuueeyefNmGhoaWpzHpaXjjj76aH79619zwQUXcOaZZzJ8+HDef/993n///Z3JUlVVFStWrEj6xLbWtWSMMYZl27dQXldDfjCLmnA9C7YWpTukXi0nJ2fn/RtvvJHjjjuOhQsX8sYbb7Q4z0owGNx53+v1EolEEj7fVVddxS9+8QsWLFjAQw891OI5Wjru+uuv59FHH6W2tpajjz6apUuXoqr8/ve/p7CwkMLCQlauXMlll12WcEyJskTGGGN6uWgsxsS1Swl4nUb6kM/PJ+uWE47aWJnuoLy8nGHDhgHOlUYdNX78eCZPnkxJSQnhcJiXXnqp2XM8+eSTO7fn5eXtHO/S2nGrVq3i4IMP5ne/+x1HHHEES5cu5eSTT+bxxx+nqqoKgI0bN1JcXPylMjvLupaMMaaXW7xtE5X1teQHswAIeH1U1NeyYGsRh+22R5qj61ojR+Yn/fLrzrruuuu4+OKL+ctf/sJ3vvOdDpczdOhQbrnlFo466ij69u3L2LFjd+675ZZb+P73v0+/fv04/vjjWbNmDQCnnXYaZ599Nq+//jr33Xdfi8f93//9HxMnTsTj8XDggQdyyimnEAwGWbJkCUcddRQAubm5PPPMM+y1114cffTRHHTQQZxyyinceeedHa8cQDJt/rlx48bprFmz0h2GMcb0CJFYlAdnTiIcjRD0fTEwtCEaQRB+duTx+L09d6zMkiVLOOCAA9IdhonT3O9ERGararPXmlvXkjHG9GKLijdS1VC3SxIDTqtMbaQho8bKqCqZ9uXcdJ51LRljTC8ViUWZvHY5Qa+/2f3OWJllHDx4eFpaZaKxGGtKt1EbCdMQjVAXCVMTbqA23EBdJExtJEx9JEx9JEJ9NEI4GmXP/gWce9CRXR6rSR9LZIwxppdasLWImnD9zrExTTWOlZm/ZQOHDxvZtcEBC4qLeHv5fLziIea2tHhEEBE8TW5Br48sn581pdvYUlXOkNw+XR6vSY82u5ZE5C4RObArgjHGGNM1wtEon6xdTsjXfGtMo5DPz5T1XX8FUzQWY+q6FWT5AuQFQ/QJZdEnlEVeMERuIEi2P0DI5yfg9eHzePF6PIib5Hy+YVWXxmrSK5ExMkuAh0XkcxG5QkQszTXGmAw3f8sGaiMNOy+5bknA66MuEmbelg1dFJlj2fYt7tid9nUc5PiDLN2+hbK6mhRFZrqbNhMZVX1UVY8GfgiMBOaLyLMiclyqgzPGGJN8DdEIU9a33RrTKOTzM2XdchqiiU+w1hkxVT5Zt6zNJKs5HhEUmL1xbdLjMt1TQu8SEfEC+7u37cA84Nci8lNVPS+F8RljjEmywi3rqYuEWxwb01T8WJlxw5qfuj6ZVu0opqyuhrxAqEPPz/EHmLN5HV8dsTdZ/kC7nvvQzEnsqK3q0Hmb0z8rl58ecWyL+0tKSjjhhBMA2LJlC16vd+cSAjNmzCAQaD3+SZMmEQgEml2d+oknnuDaa69l+PDhVFVVseeee3LzzTe3uZL1a6+9xr777svo0aPbeHWJW7t2LZ999hk/+MEPklZmozYTGRG5BzgV+Bj4q6rOcHfdISLLkh6RMcaYlKmPhHeOPWmPLF+AKetWMGbI7h1qKUmUuq0xPo8XEelQGV6Ph6jGmLdlA1/Zfa92PXdHbRV9QtkdOm9L5bVmwIABOxdPvOWWW3ZZoDERkyZNIjc3t8Xk5Nxzz+Vf//oXABMnTuTMM89k4sSJrc6d89prr3HqqacmPZF59tlnU5LIJDJGZj4wVlV/GpfENLJr3IwxJoMUbllPQzTS7sup/V4v9dEwhVvWpygyx7ryErZVV5KVYLdXS7J8AaYXrcrIZRZmz57NMcccw+GHH87JJ5/M5s2bAbj33nsZPXo0Y8aM4bzzzmPt2rU8+OCD3HPPPYwdO5YpU6a0Wu5xxx3H5ZdfzsMPPwzAI488whFHHMEhhxzCWWedRU1NDZ999hkTJkzg2muvZezYsaxatarZ4wBeeuklDjroIA455BC+8Y1vABCNRrn22ms54ogjGDNmDA899BDgrMU0ZcoUxo4dyz333JPU+kokkblQVavjN4jIRwCqWp7UaIwxxqRMXSTM1PUr290a0yjLF2DquhXUR5pffbmzVJUp65bjFU+HW2Ma+b1e6iJhlm7fnKTouoaqctVVV/Hyyy8ze/ZsLr30Um644QYAbr/9dubOncv8+fN58MEHGTlyJFdccQW/+tWvKCws5Otf/3qb5R922GEsXboUgDPPPJOZM2cyb948DjjgAB577DG++tWvcvrpp3PnnXdSWFjIXnvt1exxALfeeivvvfce8+bNY8KECQA89thj9OnTh5kzZzJz5kweeeQR1qxZw+23387Xv/51CgsL+dWvfpXUOmuxfVBEQkA2MFBE+gGN76p8YFhSozDGGJNyczatIxyNkJXg2Jim/F4vtfUNFG5Zz/jh7euyScTmyjI2VpR2eGxMUwGvj0/Xr+DAQcPwdDIx6ir19fUsXLiQk046CXBaOIYOHQrAmDFjuOCCCzjjjDM444wzOlR+/MzHCxcu5I9//CNlZWVUVVVx8sknN/uclo47+uijueSSSzjnnHM488wzAXj//feZP38+L7/8MuAsMrlixYo2x/p0RmsdnT8FrgF2A+bEba8A/pWyiIwxxiRdbbiBaRtWkt3Owa9NZfkCfLp+JWOHjPjSsgadNXX9ip0T3iVD0OujrK6GNaXb2Kv/oKSUmWqqyoEHHsi0adO+tO+tt97ik08+4Y033uC2225jwYIF7S5/7ty5O8fHXHLJJbz22msccsghPPHEE0yaNKnZ57R03IMPPsjnn3/OW2+9xeGHH87s2bNRVe67774vJUUtlZ0MLXYtqeo/VXUU8FtVHRV3O0RVLZExxpgMMmvTWsKxKD5P55Ya8Hu9NEQjSR8rU1xVwerSbeT4g0krU0TwiodP16/MmDWYgsEg27Zt25nIhMNhFi1aRCwWY8OGDRx33HHccccdlJeXU1VVRV5eHpWVlQmVPXnyZB5++GF+8pOfAFBZWcnQoUMJh8P897//3Xlc0zJbOm7VqlWMHz+eW2+9lYKCAjZs2MDJJ5/Mv//9b8Jhp/tx+fLlVFdXtyvO9mqta+l4Vf0Y2CgiZzbdr6r/S0lExhhjkqom3MDnRas73RrTKBWtMtM2rERIXmtMo2x/gE2VpWypqmBoXtvzufbPyk365dft4fF4ePnll7n66qspLy8nEolwzTXXsO+++3LhhRdSXl6OqnL11VfTt29fTjvtNM4++2xef/117rvvvi+Nk3nhhReYOnUqNTU1jBo1ildeeWVni8yf//xnxo8fT0FBAePHj9+ZaJx33nn85Cc/4d577+Xll19u8bhrr72WFStWoKqccMIJHHLIIYwZM4a1a9dy2GGHoaoUFBTw2muvMWbMGLxeL4cccgiXXHJJUsfJSEtZqoj8SVVvFpH/NLNbVfXSpEXRDuPGjdNZs2al49TGGJORJq9dyrQNqxKeNyYRFfW1HDNyf45q5+XNzdlRW80jsyaTEwimZCxLZUMd+/QfzJmjD//SviVLlrR6KbLpes39TkRktqqOa+74FltkVPVm9+ePkhqhMcaYLlPdUM+MojVkJ7HLBpxWmc82rOTQoSMSniG4JdOLnLWRUjUgN8cfZEXJVspqa+iblbw5Ykz3kMiikb8UkXxxPCoic0Tkm4kULiLfEpFlIrJSRK5v4ZhzRGSxiCwSkWfb+wKMMca0bMbG1UQ1hs+TyGwbifN7vYSjEeZuWtepcirra1mwtYicFF7V0rhswcxNa1J2DpM+ibyzL1XVCuCbwADgIuD2tp7kLmtwP3AKMBo4X0RGNzlmH+D3wNGqeiDOVVLGGGOSIBqLMXvTuqS3xjTK8gWYsn45a0q3dbiMGRvXoKp4JLmJVlM5/gBzN6+nJtzwpX2ZMhC4N+jI7yKRd05jW9+3gadUdVHcttYcCaxU1dWq2gA8D3y3yTE/Ae5X1VIAVS1OLGxjjDFt2V5TSSwFrTGN/F4vfo+XFxbOYEbR6nZ/CNU01DNn07qkXqnUEq/HQ0xjzNu869VWoVCIkpISS2a6AVWlpKSEUKh98wglsmDGbBF5HxgF/F5E8oBYAs8bBsSv+14EjG9yzL4AIvIp4AVuUdV3mxYkIpcDlwOMGDEigVMbY4zZULGDWIo/oIM+P16Pl49WL2FLVQWn7HNwwssfzN68jqjG8KYo0WrKWbZgNeOGjdoZ4/DhwykqKmLbto63KpnkCYVCDB8+vF3PSSSRuQwYC6xW1RoRGQAkawCwD9gHOBYYDnwiIgeraln8Qar6MPAwOFctJencxhjTo60oKe70vDGJ8Hk85AVDLNq2ke01lZx94Lg2r5Cqi4TdQcipGxvTlN/rpaK+liXbNjFmyO7ONr+fUaNSv6K3SZ0202BVjQFbgdEi8g3gQKBvAmVvBHaPezzc3RavCJigqmFVXQMsx0lsjDHGdEI0FqOofAfBFK5UHc8jQn4gxPaaSh6fM4WiitJWj5+/ZQPhWKRLEq14jcsWpLqlynSdRK5augP4FPgjcK17S2SN8ZnAPiIySkQCwHnAhCbHvIbTGoOIDMTpalqdYOzGGGNaUFxdiaJd1m0Dzky6uYEQMVX+O28ahZvXNzv2JByN8mknFq/sjJDPT3l9Hat22JDMniKRVP0MYD9VrW9PwaoaEZFfAO/hjH95XFUXicitwCxVneDu+6aILAaiwLWqWtKuV2CMMeZLNpSXpK3VIeTz4/N4eGfFArZWV3DinqN3SagWFhdRHw0ndYK+9vB5hM/Wr2Tv/oOSPpOw6XqJJDKrAT/QrkQGQFXfBt5usu2muPsK/Nq9GWOMSZKVO7pmfExLfB4vecEQczatY1t1Jd874DByAkGisRhT160g6E3ugpPtkeULsLmqjM2VZeyW3y9tcZjkSKTNsQYoFJGHROTexluqAzPGGNMx0ViMjRWlhHxdMz6mJR4R8oMhNlaU8p+5U9laVc6y7ZupDtcTTGNsIoIgTHNnFDaZLZF30gS+PLbFGGNMN7W1qhwl9ZPMJUJEyAuGqAk38FThZ4R8fgJdNAC5NTkBZ9mCHbXV9M/KSXc4phPafDep6pMikgWMUNVlXRCTMcaYTthQUUos1r2uysn2B2iIRqhuqCc/lJ6xMfGcdZ2E6RtW8e19x6Q7HNMJiVy1dBpQCLzrPh4rItZCY4wx3dSKkq0JT0rXlQJeX7dIYhrlBoLM37qBoood6Q7FdEIi7Y634Cw3UAagqoXAnimLyBhjTIdFYlE2VZYR7OSK1L2BRwSfx8uby+YTiUXTHY7poEQSmbCqljfZlsgSBcYYY7rYlqoKQN2uE9OWbH+A0tpqZhTZytiZKpFEZpGI/ADwisg+InIf8FmK4zLGGNMBReWpX1+pp8kJBJm6fjklNVXpDsV0QCKJzFU4yxLUA88BFcA1KYzJGGNMB60o2Yo/jfPHZCJndXDh7eXzbRXsDJTIVUs1wA3uzRhjTDcVjkbZXFVGtj+Y7lAyTo4/QFFFKQuLizh48O5tP8F0G622yIjIxSIyR0Sq3dssEflhVwVnjDEmcVuqnOGMNj6m/USELL+fD1Ytpqqh3RPZmzRqMZERkYtxupB+A+wGDAOuA34pIhd1SXS9VENDlFdeWc7ZZ7/Ov/41h82brd/WGNO2DRU7iHaz+WMyScDroyEa5aPVi9IdimkHaak/UESmA+ep6tom20cCz6vqV1IeXTPGjRuns2bNSsepUy4cjvL222u4//65lJbWkZ3to6Ymgohw7LG7c+GFozn44IG2yJkxpllPFn7K9uoqsvx26XVHxVSprK/j3IOOYM/+g9IdjnGJyGxVHdfcvtbGyOQ3TWIAVHWtiOQnKzgDkUiMDz9cx733zmHbthry8gIMGeJMmZ2fHyQajTF58gYmTlzPXnv15dJLD+LYY0cQCNiAPmOMIxyNsqWynJyAjY/pDI8IQZ+Pt1cs4PJxx3SL5RRM61r7DdV2cJ9JUDQaY+LE9dx771w2baoiL8+/M4GJ5/V6GDQoG1Vl06YqbrhhKnl5AS66aDTf/e7e9O/ffWbKNMakx+aqMkTExsckQcjnp6K+lqnrVnD8ngekOxzThtYSmQNEZH4z2wWb2bdTVJVPPinin/+czfr1leTk+Bk6tO1Fy0SEPn2C9OkTpLY2wv33F/Lgg/P49rf35Ac/OIB99rHl6I3prTaU7SAWs7lKkyXHH2TmxjUcOGg3Buf2SXc4phWtJjJdFkUvoap89tlG7r13LqtWlZGd7WPIkOwOjXnJyvKRleUjEonx1lurePPNVYwbN4R//OMYcnICKYjeGNOdrdixlYDPukGSxevx4BHhreXzuXjs0Xg96V9J3DSvxXe9qq7rykB6uvr6CFdf/TGzZ28hK6vjCUxTPp+HwYNzUFVmztzCDTdM5a67jsXrtf90xvQWDdEIW6sqbHxMkmX7A2ytqmDO5nUcMWxUusMxLbBPuy6gqvztb58za9YWhgzJoW/fUNKvPBIRhgzJZsqUIu6/vzCpZRtjurdNlWWI2PwxySYi5AQCTFqzlPK6mnSHY1pgiUwXeOWV5bzxxiqGDMlJ6aXTIsKgQdk88cRC3nprVcrOY4zpXjaU2fpKqeLzeFFV3lu50JYv6KbaTGRE5DQRsYSng+bP38bf/z6DAQOy8HhS/23J5/MwYECIW2+dxvz521J+PmNM+q3YsdUuE06hnECQVaXbWL59S7pDMc1IJEE5F1ghIn8Xkf1THVBPUlxcza9/PZGsLB/BYNfN+RIKOef75S8/tlmBjenh6iNhtlVXErREJmVEhJDXzzsrF1Abbkh3OKaJNhMZVb0QOBRYBTwhItNE5HIRyUt5dBmsoSHKb387mcrKBvLzu34AnnOJdphf/vJjqqvtP54xPVXj+Bib8Tu1gj4fdZEwn6xbnu5QTBMJdRmpagXwMvA8MBT4HjBHRK5KYWwZS1W5884ZLFq0nYKC9E1WV1CQzerV5dx006fEbP0VY3qk9eU2Pqar5AaCFG5ez9aqinSHYuIkMkbmuyLyKjAJ8ANHquopwCE4C0q29txvicgyEVkpIte3ctxZIqIi0uw6Cpnm9ddX8sorKxg8OLWDexMxeHA2Eydu4N//npvWOIwxqbGyxMbHdBWPeBDBBv52M4m0yJwJ3KOqB6vqnapaDKCqNcBlLT1JRLzA/cApwGjgfBEZ3cxxecAvgc87EH+3s3DhNv76188ZMCCE15v+pl6PRxg8OJvHH1/Iu++uSXc4xpgkqouE2VZTZeNjulCOP8jGilKWbt+c7lCMK5FEZouqfhK/QUTuAFDVj1p53pHASlVdraoNON1S323muD8DdwB1iYXcfW3fXsOvfjWJUMhLKNR9/rD4fB769Qtx882fsnChXclkTE+xqbIMj42P6VIiQsjn54NVi6iPhNMdjiGxROakZradksDzhgEb4h4Xudt2EpHDgN1V9a3WCnIHF88SkVnbtnXPD+KGhijXXTeZ8vJ6+vTpfrNrNl45dc01E9m6tTrd4RhjkmBd2XYbH5MGQZ+PmnADnxetTncohlYSGRG5UkQWAPuLyPy42xqgucUk28Wdm+Zu2hhnA6CqD6vqOFUdV1BQ0NlTJ52qcs89s5k3bxuDBnXflaj79AlSVeVcyVRTY98kjMl0K3cUW7dSmuT4g0wvWsWOWvtimG6ttcg8C5wGvO7+bLwd7l6S3ZaNwO5xj4e72xrlAQcBk0RkLfAVYEImDvh9663VvPjiUgYPTs76Sak0cGCIlSvLuPlmu5LJmExWG26gpKbKBvqmidfjQRU+XLXYBv6mWWuJjKrqWuDnQGXcDRHpn0DZM4F9RGSUiASA84AJcYWXq+pAVR2pqiOB6cDpqjqrQ68kTZYsKeEvf5lO//6hjFioUcQZ/Pvxx+t55JF56Q7HGNNBzvgYT7f/8tST5QaCrC4tZk3p9nSH0qu11SIDMBuY5f6cHfe4VaoaAX4BvAcsAV5U1UUicquInN6pqLuJHTtq+dWvJuL3S7ca3NsWj8dZk+nRRxfYMgbGZKh1ZSU2PibNRAS/x8t7KxcSiUXTHU6vJZnWJDZu3DidNSv9jTaqypVXfsCcOcUMHpyd7nA6pKysjv79s3jhhdMyKhEzxsBDsyZR09BA0Gf/d9Otor6W40cdwJHD90x3KD2WiMxW1WaHnrQ22Pew1m6pCzczTJiwipkzt3Trwb1t6ds3xKZNVTz2WKfHbhtjulBNuIHS2moC3q5bw820LNsf4JN1y6lqyPhZRDJSa6n8Xa3sU+D4JMeSMYqLq7nzzpn06xfK+P7pgQOzePLJxZxwwh7sv/+AdIdjjEnApopSPCIZ//enp/B5vNSGw0xcs5TT9hub7nB6nRYTGVU9risDyRSqyu23z6ChIUq/ft1vvpj28vk8+P0ebrzxU/773+8QCNg3PGO6uzU2f0y3kxMIsqh4I4cP3YPd8vulO5xepcVERkSOV9WPReTM5var6v9SF1b3NXHieiZP3sCQITnpDiVp+vcPsWZNOU89tYgf/3hMusMxptuoq4uwbNkOKisbyMnxk53td3/6yMnxEwh409IqsmpHMSGfv8vPa1rmEcErHt5ZuZAfHXo0Hun+V7H2FK11LR0DfIwzd0xTCvS6RKasrI4//3k6ffoE8Xh6VpPugAEhHnlkPscdN4K99uqb7nCMSYuamjBLlpQwd24xU6duZMmSEhrzlMaERVWJxSAaVbxeyM72k5sbIDfXT35+gPz8IEcdNZQTTxyZklm+qxvqKaurJS+Q+S3CPU22P0BxVQULt25kzJDd236CSQq7aqkdbrxxKu++u6ZHtcbE2769llGj+vDUU9/G57NvE6bnq65uYNEiJ3GZMqWI5ctL8XggElFycnzk5ARaXfw1FlOiUSUajRGJKLGY87OhIYrX6+H440dw1ln7cOihg5P25Wd5yVZeXTKbvEAoKeWZ5GqIRlBVrjjiOLL8gXSH02O0dtVSm9fticgA4GbgazgtMVOBW1W1JKlRdnPTpm3k7bdX99gkBpxWmWXLSnnuuSVcdNGB6Q7HmJRYsqSEjz5ax9SpG1m1qgyvV4hEYuTkBCgoyGpXwuHxCB6P4Pd/OfGPRGJ89NE6PvpoHf37hzj//AP41rdGMmhQ5/6GrC3dZjPJdmMBr4+K+lqmrl/BSXvZ39Gu0GaLjIh8AHwCPONuugA4VlVPTHFszUpHi0x1dQNnnTWB2toI+fk9O8NuaIhSXt7Aiy+exogR+ekOx5ikUFXmzNnKQw/NZ+7crahCbq4z3qUruomrq8NUVjYgIhx11G6cc85+jB8/tN0tnzFV7p/xMdFY1JYm6MaisRg14QYuO+zrDMzJS3c4PUKnWmSAoar657jHfxGRc5MTWma4//5CSkpqe3RrTKPGq5ZuuulTHnvs5IxYdsGYlsRiymefbeTf/57H8uU78Ps9aVkTLSfHSZpiMWXGjM1Mm7aJ3Fw/3//+vpx66l7svntiXxo2VpRSE663bqVuzutxlo54d+VCLhjzFbtMPsUS+ZR6X0TOExGPezsHZ9mBXmHevGJefHEZBQWZO/Fdew0cGGLBgm38738r0h2KMR0SicT44IO1nH3261xzzUTWrStn8OBsBgzISuuHiscjDByYxeDB2Xi9wuOPL+TMM1/nL3+ZRlVVQ5vPX7C1yOngN91ejj/AhvIdLN++Jd2h9Hgtdi2JSCXOfxkBcoCYu8sDVKlqWvodurJrqa4uwrnnvsGOHbX07du7vgHV1UWoqYnw0kuns9tuuekOx5iE1NdHeOedNTz00Dy2b68lO9tHXl6gW38jjkZjbNtWy4ABWfz5z0dzxBFDmz2uIRrh3ukfEvT68HqspTQT1EfCeD1efjruGIJ2uXyndGiJAlXNU9V896dHVX3uzZOuJKarPf74AjZurOx1SQxAKOQjElFuvfUzG1hour3q6gaefXYxp5zyCrfdNp26uihDhuSQnx/s1kkMgNfrYciQHGprw1x55YfcccfnVFd/uXVm1Y5iIrGoJTEZJOjzUxOu57MNK9MdSo+W0P8IEeknIkeKyDcab6kOLN2WLdvBE08sZODAzFwQMhkKCkLMnLmFN95Yle5QjGlWOBzdmcDcc89sPB5hyJAccnMz79tvfn6QQYOyeOml5ZxzzhsUFhbvsn/O5nX4LInJODn+IDOK1rC9ujLdofRYbf6vEJEf41y19B7wJ/fnLakNK70ikRg33/zpzun7eysRoV+/EHfeOZPi4up0h2PMLgoLiznvvDe5667ZBINehgzJISsrs6/k8Xo9DB2aQ2VlAz/+8XvcdddMamvDVNTXsqF8B1m+nn3VZE/UOPD3nRULrHU7RRL5lP4lcASwzl1/6VCgLJVBpduzzy5m5coy+vfvfV1KTWVl+aivj3LbbdPtP6HpFnbsqOWmmz7lxz9+j61bq9lttxxCocxOYJrq0ydIQUEWzz23lHPOeYP35ywD6PbdZKZ5Of4ARZWlLNq2Kd2h9EiJJDJ1qloHICJBVV0K7JfasNJn3bpyHnhgHgMGZP7K1skyaFAWU6du5P3316Y7FNOLRaMx/ve/5Xz3u6/xzjurGTQom379eu6XDZ/PaZ0pK6vjlc8WsG1zHbGYfZnIRCJCls/PBysXURtu++o00z6JJDJFItIXeA34QEReB9alMqh0icWUW2+dBmCrQMcREfr2DXLbbdPZutW6mEzXW7KkhIsuepu//nX6zm6k1pYO6EkGjgyQ3VfYtKGGOXO2JnSZtul+Al4fDdEwn6xdlu5Qepw2ExlV/Z6qlqnqLcCNwGPAGSmOKy0mTFhJYeE2Bg7sud/yOio72099fZQbb5xKNBpr+wnGJEF5eT1/+9vn/PCHb7N2bXmPGAfTXqE9IoAQCnlpaIhSOHcbGzdWpTss0wE5gSCFWzawubI83aH0KIletXSYiFwNjAGKVLVHfiX45JMiQiGvdSm1oKAgi9mzt/Lss0vSHYrp4VSVt99ezRlnvMb//recgoKstE9mlxYeJWuPKDH3L67f78Uf8LB6dRkrVpSi1tWUUTziwSPCuyvmE1P7QpgsiVy1dBPwJDAAGAj8R0T+mOrA0qW3/Z1sDxFnVtL77pvL0qW9as1Q04VWry7j0kvf5cYbp+Lx4HYj9c6rB4ODY4hXIfbFHyaPRwgGvWzZUs38BdsJh+0DMZNk+wNsqapg/paidIfSYyTy1+EC4AhVvVlVbwa+AlyU2rBMdxUIeAkEPFx33SfU1ITTHY7pQRoaojzyyDzOP/9Nli7dwdChOWRnZ958MMmUNTKySxLTSEQIBb1UVjYwd+5WamoiaYjOdISIkO0P8NGaxVQ31Kc7nB4hkURmExA/aCQIbExNOCYT9OsXYtOmKu6+u2tXITc918KF2zjvvDd46KF59O0bZODAXtiN1IQnqASGxIi19FknTstMOBxj7tytlJbWdWl8puP8Xi+RaIyJa5amO5QeocVERkTuE5F7gXJgkYg8ISL/ARbSw+eRMW0bNCiLV19dweTJG9Idislg1dUN/OMfM/nRj96luLiGoUNz7YpBV3C3CF8sd9eyQMCL1yMsXFjCxiKbPTZT5ASCLCwuoqhiR7pDyXittcjMAmYDrwJ/ACYCk4AbgNcTKVxEviUiy0RkpYhc38z+X4vIYhGZLyIficge7X4FJi28Xg99+gS56aZPbdZf0yHTpm3krLMm8PzzSxk4MKtHzwnTfkr2XlE0klirlNfnIRDwsHpNOcuX2yDgTOARwefx8vby+URjNs6pM1q8jlFVn2y8LyIBYF/34TJVbXNwhIh4gfuBk4AiYKaITFDVxXGHzQXGqWqNiFwJ/B04t/0vw6RDTo6f4uIabrzxUx544MReOyDTtE9paR3/+MdM3ntvLTk5foYOzUl3SN2OL1/x5sWI1SbevdY4CHjr1mpqa8OMHj2wVy+xkgmy/QF21FQzZ/M6jhg2Kt3hZKxErlo6FliBk5Q8ACxPcNHII4GVqrravVz7eeC78Qeo6kRVrXEfTgeGJx666Q4KCrKYOXMLzz1nl2Sb1qkq7723hu9973U++GAdgwdnk59vawc1J7R71L3XvnFCXwwCDjN3zlaqq21AfneXHQgwee0yKutr0x1KxkokXb8L+KaqHqOq3wBOBu5J4HnDgPgBFEXutpZcBryTQLmmGxERCgqyuPfeuSxbZn29pnmbN1dx1VUf8Yc/TMHrhcGDs/F4evdg3haJkjUqQqyhg/XTOAg4EqOwsJgdO2wQcHfm83iJxmJ8uGpx2webZiWSyPhVdeecyqq6HEjqNZEiciEwDrizhf2Xi8gsEZm1bdu2ZJ7aJMEXl2RPtkuyzS7C4SjPPruYs856nZkzt9gl1QkIFMQQv0K0c4le4yDgRYtKKLJBwN1abiDI0u1bWFNqn28dkUgiM1tEHhWRY93bIzgDgduyEdg97vFwmrlsW0ROxBlAfLqqNnuhoao+rKrjVHVcQUFBAqc2Xa1fvxAbN1bxf/83O92hmG5i9uwtnHPOG9x992xyc/0MGpTd6y+pTkTWyAhocuqpcRDwmjXlLFu2wxad7KZEhIDXyzsrFhCORtt+gtlFIonMFcBi4Gr3thi4MoHnzQT2EZFR7mDh84AJ8QeIyKHAQzhJTHF7Ajfdz6BBWbzyynI++cQuye7Niouruf76T/jpTz9g+/Zahg7NIRjsXesjdZT4leBurcwd0wGNg4CLi2tZMH+bzQTcTWX5A1TW1/H43Cks376FmFrSmahW/7q4Vx7NU9X9gbvbU7CqRkTkF8B7gBd4XFUXicitwCxVnYDTlZQLvOR+U1uvqqd34HWYbiD+kuyXXjqdgoLsdIdkulBDQ5QXX1zGAw/MJRJRGwfTAcGhUWeZlCS1yDRyBgF7qKxyBgEfeNBAcnKsi6+7yQ0Eqaqv439LZjMgK5djR+3P3v0HdbolMxKLsrKkmMIt66mLhPF5PPg8Xvfm3Pd7nfv+nfe9BL0+DijYDb+3e8/t1Goio6pRdx6YEaq6vr2Fq+rbwNtNtt0Ud//E9pZpurecHD9bt9Zw441TeeCBk+yDrJeYMWMzf/3r52zYUMGAAVkEg937D193lb1XhFiqehZ2zgQcpbCwmAMOGED//jZ3T3ciImT5A4RUqaiv5ZXFsxiYncexI/djrw4kNCU1VczbsoHCLetpiEbwigefx4PiXEX45Z9OK5BzXwBl0bZNnDX6cALe7tuqmkhk/XBm9p0B7Jz5zFpOTEsGDXIuyX7mmUX88IcHpTsck0Jbt1Zz992z+PDDdWRn+9ltt9x0h5SxvDkxfH2VWIqvwvX7vUQjMRYtKmHUqHyGD89L7QlNu8UnNOV1Nby0aBaDcvM4duT+7NmvoNWEJhyNsqJkCzM3rmVzVRngzFcT8mW1Ow5VZV3Zdl5aOJOzDxxH0Nc9W/ESSWRuTHkUpkdpXCX7n/+cQ9++IU4/fe90h2SSrKEhyvPPL+HBB+cRicQYMiTHWt86yZk7Rkls6GLneH0exKOsWVNOdXWYffbpZ7+/bmhnQuNTSmtreGnRTAbn5HPsqP0Z2XfgzoRGVdlWXcm8LRuYt3UDkVgMn8dDXiDUqW4pESEvEGJDxQ5eWDiTcw46glA3TGZaTGREJIQz0HdvYAHwmKraEqsmIYGAlwEDQvzpT5/h9Qrf+c5e6Q7JJEEspkyevIF77pnNpk1V9O8fsm6kpFCy9oygHZ07pgPiBwHX1UYYfaDNBNxdNa6YraqU1FbzwsIZDM3twzdG7kdVQz0zN66huLoSwRk0nJ3E32NjMrOpspTnF3zOuQcdSZa/e01k2VqLzJNAGJgCnAKMBn7ZFUGZniEY9NG/f4ibb/4Mn8/DySfbFNyZSlWZOrWIe++dy5o15eTk+GxpgSTyD4zhCdKuJQmSwQYBZ5b4hGZbTSUvLpyJovg9XvICwZRNb9CYzGypKue5BdM57+CvkN2NkpnWEpnRqnowgIg8BszompBMTxIK+ejbF264YSo+n4cTTrB1QTOJqjJ9+ibuvXcOK1aUkZXlY8gQmw8m2bL2SOPcITYIOOM4CU2wy8+ZFwixrbqK/86fxg8O/go5ga6NoSWttT/tnKLVupRMZ2Rl+ejbN8j1109h8mSbYyYTqCozZ27mhz98m6uu+pgNGyoZMiSbvn1T962vtxKfEhoeJZbmlQT8/saZgLezZnW5TZ5nvkREyAuGKK2t4Zl506hq6B7LX7SWyBwiIhXurRIY03hfRCq6KkDTM2Rl+cjP93PttZOZOrUo3eGYFqgqc+du5bLL3uVnP/uQ1avLGTIkm379Ojdo0LQsMCTq/CVO8twxHeH1eQgGvBRtrGTu3GJqauw7rPmy3ECQ8roani6c1i0Wu2wxkVFVr6rmu7c8VfXF3c/vyiBNz5Cd7Sc3189vfjOJ6dM3pTsc08T8+dv46U/f5yc/eZ+lS3cweHA2/ftbApNKvvwYeQdH0G40K714hFDIR11dhLlztrJpU1W6QzLdUG4wRGVDLU/Nm0Z5XU1aY7Eh6qZL5eT4ycnx88tffsyMGZvTHU6vF4spn3++mZ/97AMuu+xdFizYzpAh2QwYkGUJTEopoRER+h9fjyegXXq1UqICAS8+v4dVq8pYuGA7DQ3dKNsy3UJuIERNQz1Pz5tGaW11209IEUtkTJfLyfGTne3j6qs/ZvbsLekOp1cqL6/n+eeXcuqp/+Oqqz5i7txiBg/OZuBAS2BSzqvkHxamz7gwsTDE6rtvfXs8Qijopay8ntmztlJSkv5uBNO95ASC1IYbeHreNEpq0tN6Z4mMSYvc3AChkJerrvqIwsKt6Q6nV1BVli4t4dZbP+Pkk1/mrrtmUlcXsQSmC3mzY/Q/tp7QyAjRGiCaAXXuXtUkAosXl7B8eSnRqA0ENl/ICQRpiEZ4dsH0tCx22X0XTzA9Xl5egIqKBn7+84948MGTOPjggnSH1CPV10eYNGkDTz65iBUrShGBAQOy8Pnse0xXCgyJ0OfIMCIQqxEgA5KYOF6fB49XKN5aQ1lZHQccMIC8vO4zl4hJr2x/gPK6WlQVuvhLkSUyJq3y8wNUVNRz5ZUf8MADJzFmjCUzybJxYyWvvbaCF19cTk1NhFDIy+DBNgdMlxMld3SEnP3CxBqEWCRz619ECIa8hMMx5hVuY8Qeeey+e35Xf24ZswtLZEza5ecHqaio57LL3uXcc/fnpz89xL7pdVBlZQPTp2/i9ddXMmPGFlSVfv1C5OdbfaaDJ6T0ObIB/8AY0VrpFpdYJ4Pf7yHmVdatq6CkpI699upDfn73mBzN9D6WyJhuIT8/SHa2nxdeWMpbb63muuuO5FvfGmmtBwmoqmpg2rRNvPnmKqZP34wq+HxCQUGWLQSYRv4BUfp+pQHxQ6wGMq0rqS2NA4FrasLMm7edPn0CjBrVx76EmC5niYzpNnw+D0OG5FBdHeaPf5zCiy8u5YYbvsLee/dLd2jdTkvJy4ABIbxeG/uSXkrW3hFnfpgIxOp6VgKzCxECAS+oUlnZwLzCbfTtF2TkyD7k5tqaTaZrWCJjup3Gy7OXLCnh/PPf5PzzD+Dyy8eQm9u7v+lZ8tL9+fJj5I4JExgUdRKYWA9OYuK5CY2qUl5Wz9y5xQzoH2KPkfm2CKVJOUtkTLckIhQUZBOJxHjuuSW89dZqrr/+SE48cY9e092kqqxZU05hYTETJ25gxgxLXrorT3aM3NERQrtHQTPzqqRkEBECQSeh2VFax44ddQwYGGLkyD5kZdnHjUkNe2eZbq2xu6mqKsz113/CoYcO5g9/GM+ee/ZNd2hJp6ps3lxNYWExU6YUMW3aJmprI8RiSiDgteSlG5KAkrNvmOy9oyBKrLZ3JjBNiTv3jKpSUlJHyfY6CgZlscce+YRC9rFjksveUSYj5Ob6ycnxsXDhds47700uvHA0P/rRQRk/sLCkpJb587fx6acbmTp1Izt21LqtLh7y8wMZ//p6KvEqWXtGyDkggnjV6UZSSzKbik9otm2rZdu2WgYNymbQoGz65AcQG4xuksASGZMxnO6mLCKRGE89tYinn17E6NEDOP74EYwbN4T99uvfrSd5q6uLsG5dBWvWlLNw4XamTt3Ixo2VeDyCqjOnzqBBNs9LtyZKaPcouQeH8QQgVg/a0H3fc91FfEJTXFxDcXENHo/TRVpQkE3fvkG7ws50mCUyJuP4fB6GDs0hFlPWri3nvvvm4vUKPp+H8eOHctxxu3PYYYPZbbfctCQFqkppaR2rV5ezalUZ8+ZtY/Hi7WzcWIXX6yEWi6HqzGxsE9RlCiUwJEremAi+nBixsLjdSKY9GhMacBYsbWylEYH+/UMMGpRN374hvF6rW5M4S2RMxvJ4hPz84M6JuMLhGNOnb2LKlCIABg7M4phjdudrXxvGIYcMSmo3TSQSo6ysjrKyesrK6ncmLoWFxSxduoOqqgZ8Pg/hcBS/30t2to9Bg7LtW2em8SiBghg5B4Tx91c0DNFaa4FJBo/ni6RGY8qOHXWUlNQB0K9fkEGDsunXL9StW1lN92CJjOkx/H4PAwZkAU6rSF1dlFdeWc6rr64gFlP69nUm3cvO9pOb6yc3N0Benp+8vAD5+UHy8px9WVk+srJ81NdH2bGjjuLiGrZsqaa4uIbt22spKamjutpJVBq7hVSVaFTJyvKRne2zlpYMJj4lMChGaHiE4NAYiIJKj5zUrrsQjzsfDc7/JefLQT0AeXl+8vOD5Ob6ycpypmawLwQmXkoTGRH5FvBPwAs8qqq3N9kfBJ4CDgdKgHNVdW0qYzK9g4jsTEjAacaORGJUV4epqGggFosRjerOWyzm3DweweMBj8eDqhKJKB6PkyT5/R58Pg/Z2T7y8/2WqPQgnqASGBQlNCJKoCC2c3usHhvE28VEdk1qaqojVFaEQZw0MqYQDHrcLyKBnfNOhYI+yzN7qZQlMiLiBe4HTgKKgJkiMkFVF8cddhlQqqp7i8h5wB3AuamKyfRenrhvfMYAeLNjBIZEyRoRw9fPTV5iEKsD+0TsHkQEn1/wxc+pp0o0BqWldZSU1O7yhSI720dOtp9A0Esw6HW+gPi8+Pwe/D4PPr/Hxt/0QKlskTkSWKmqqwFE5Hngu0B8IvNd4Bb3/svAv0REVFVTGFeLVBQJxlB/NB2nN8a0pIXPnp2bPU6XkPhAvM59jxf3sTr7/OAJgMcP/v4xfLnqdAtGIFrbtOS0/AkyiRDweMHj3XXOHlWoq49QUxtB1fndioAg7m/zi21er+D3ewj4Pfh8Xjxewbvz5nFbZuNu4iRV4hE8Is5q34K76re45wEa9+Eev/N+cy9Ddnlfy85/Mlcslp7/N6lMZIYBG+IeFwHjWzpGVSMiUg4MALbHHyQilwOXuw+rRGRZKgIO9ek7JpCdZ+3IHdBQUyWB7Fz7698BVndta+nve31NlQSzc3WXA0S+eBh390uFaNy/vUx9VZUEc3vpe053eVe0dlzje2aXeurVddeGWFRjf9y8YX4rhwykyed7O+zR0o6MGOyrqg8DD6f6PCIyq7asdFyqz9MTOXW3w+quA6zuOs7qrmNEZFZNaYnVWwdY3XWciMxS1aTXXSpbHzYCu8c9Hu5ua/YYEfEBfXAG/RpjjDHGtCmVicxMYB8RGSUiAeA8YEKTYyYAF7v3zwY+Ttf4GGOMMcZknpR1LbljXn4BvIdz+fXjqrpIRG4FZqnqBOAx4GkRWQnswEl20inl3Vc9mNVdx1nddZzVXcdYvXWc1V3HpaTuxBpAjDHGGJOp7AodY4wxxmQsS2SMMcYYk7EskQFE5HERKRaRhemOJdOIyO4iMlFEFovIIhH5ZbpjygQiEhKRGSIyz623P6U7pkwjIl4RmSsib6Y7lkwiImtFZIGIFIrIrHTHk0lEpK+IvCwiS0VkiYgcle6YMoGI7Oe+3xpvFSJyTdLKtzEyICLfAKqAp1T1oHTHk0lEZCgwVFXniEgeMBs4o8lSFKYJceZVz1HVKhHxA1OBX6rq9DSHljFE5NfAOCBfVU9NdzyZQkTWAuNUtaMTk/VaIvIkMEVVH3Wvxs1W1bI0h5VR3OWLNgLjVXVdMsq0FhlAVT/BuWrKtJOqblbVOe79SmAJzozNphXqqHIf+t2bfatIkIgMB74DPJruWEzvICJ9gG/gXG2LqjZYEtMhJwCrkpXEgCUyJolEZCRwKPB5mkPJCG7XSCFQDHygqlZvifs/4Dog1sZx5ssUeF9EZrvLv5jEjAK2Af9xuzQfFZGcdAeVgc4DnktmgZbImKQQkVzgFeAaVa1IdzyZQFWjqjoWZ9brI0XEujUTICKnAsWqOjvdsWSor6nqYcApwM/drnXTNh9wGPBvVT0UqAauT29ImcXtjjsdeCmZ5VoiYzrNHePxCvBfVf1fuuPJNG7z9ETgW2kOJVMcDZzujvV4HjheRJ5Jb0iZQ1U3uj+LgVeBI9MbUcYoAoriWk5fxklsTOJOAeao6tZkFmqJjOkUd9DqY8ASVb073fFkChEpEJG+7v0s4CRgaVqDyhCq+ntVHa6qI3GaqT9W1QvTHFZGEJEcd1A+brfINwG7WjMBqroF2CAi+7mbTgDsoob2OZ8kdytBhqx+nWoi8hxwLDBQRIqAm1X1sfRGlTGOBi4CFrjjPQD+oKpvpy+kjDAUeNIdwe8BXlRVu4zYpNpg4FXn+wc+4FlVfTe9IWWUq4D/ul0kq4EfpTmejOEmzicBP0162Xb5tTHGGGMylXUtGWOMMSZjWSJjjDHGmIxliYwxxhhjMpYlMsYYY4zJWJbIGGOMMSZjWSJjjGkXEblERHZL4LgnROTsRLcnIa4/xN0fmehq9iJyjYj8MAnn/4WIXNrZcowx7WOJjDGmvS4B2kxk0uAPbR+yKxHxAZcCzybh/I/jzDNijOlClsgY04u5LRdLReS/IrJERF4WkWx33+EiMtldXPA9ERnqtqSMw5kUrFBEskTkJhGZKSILReRhd7bnRM//pXO42yeJyB0iMkNElovI193t2SLyoogsFpFXReRzERknIrcDWW5M/3WL94rIIyKySETed2dQbup4nCnTI275e4vIhyIyT0TmiMheInKsG+PrIrJaRG4XkQvc2BaIyF4AqloDrBURm/LfmC5kiYwxZj/gAVU9AKgAfuaun3UfcLaqHo7T2nCbqr4MzAIuUNWxqloL/EtVj1DVg4As4NRETtrSOeIO8anqkcA1wM3utp8Bpao6GrgROBxAVa8Hat2YLnCP3Qe4X1UPBMqAs5oJ42ggfvHJ/7rPOQT4KrDZ3X4IcAVwAM5M1vu6sT3Krq0ws4CvJ/L6jTHJYUsUGGM2qOqn7v1ngKuBd4GDgA/cBhYvX3yoN3WciFwHZAP9gUXAGwmcd782ztG4AOlsYKR7/2vAPwFUdaGIzG+l/DWqWthMGfGGAksA3DWIhqnqq275de52gJmqutl9vAp4333+AuC4uPKKgf1bickYk2SWyBhjmq5TooAAi1T1qNaeKCIh4AFgnKpuEJFbgFCC523rHPXuzygd+1tVH3c/itNa1FQticUbX1Ys7nGsSWwht0xjTBexriVjzAgRaUwmfgBMBZYBBY3bRcQvIge6x1QCee79xiRgu4jkAu25Gqm1c7TkU+Ac9/jRwMFx+8Jud1V7LAH2BlDVSqBIRM5wyw82jhdqh32x1aSN6VKWyBhjlgE/F5ElQD/g36ragJOU3CEi84BCnDEjAE8AD7qrndcDj+B8eL8HzEz0pG2coyUP4CQ/i4G/4HRjlbv7Hgbmxw32TcQ7wDfiHl8EXO12WX0GDGlHWeCMufmgnc8xxnSCrX5tTC8mIiOBN92But2eiHgBv6rWuVcLfQjs5yZFHS3zVeA6VV3RydgOBX6tqhd1phxjTPvYGBljTCbJBia6XUgC/KwzSYzrepxBv51KZICBOFdSGWO6kLXIGGOMMSZj2RgZY4wxxmQsS2SMMcYYk7EskTHGGGNMxrJExhhjjDEZyxIZY4wxxmQsS2SMMcYYk7EskTHGGGNMxrJExhhjjDEZyxIZY4wxxmQsS2SMMcYYk7EskTHGGGNMxrJExhhjjDEZyxIZY4wxxmQsS2SMMcYYk7F86Q6gvUTkEmBkmsMwxhhjTBdR1Vta2ieq2oWhGGOMMcYkj3UtGWOMMSZjWSJjjDHGmIxliYwxxhhjMpYlMsYYY4zJWJbIGGOMMSZjWSJjjDHGmIxliYwxxhhjMtb/Aw2nQIpag2QCAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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FF1wwOy4ujkceeaTmXXfdddhzzz33y5AhQw4bOnTo4lNOOWXz+vXr4xITE3PvvffeX4cMGVL7s88+m583n3/+85/VTzrppPUPPvjg8uzsbCLNa7KysuLat2+/6fHHH//1yiuvrPf444/Xeuihh5b179+/Qe/evVdfddVVqx977LEa/fv3rz9u3LgFRx555NYpU6ZUnDdvXoX09PQt48ePTzrxxBM3L1u2rHyLFi22AcyfP7/iN998M2fdunXx6enpzW+88caVFSpU2NkJ/ttvv01ctmxZuXnz5s0EWLVqVXx0Wbds2WLXXHPNEePHj5+dlpa2/cwzz2wUvX1v+X/66afzExMTW8+ePXsWwMCBAytFtvXq1avRDTfcsLxPnz7rtmzZYjk5OVaxYsXcsWPHzq9evXrusmXLEo455pi0Xr16rQP473//W3HEiBE/d+jQYfEf/vCHI19++eVqf/nLX9Y8/fTThz788MNLjj/++C3RZevZs+f6CRMmrExKSsq56667VkyZMqXi6NGjq0+aNGl2hQoV/MILL2zwzDPP1BgwYMDqRx555NfatWvnZGdn06FDh9QJEyZUGjx48G9PP/107c8//3zu4Ycfnr2378eyZcvKT5kyZXZCQgLt27dPefbZZxe3aNFi26efflq5f//+Db777ru5e8sjQkGFiIiISCm1cOHC8meffXa9lStXltu+fXtc/fr1twEce+yxm2644Yb6559//po//elPaxs3brzHpjLHHnvs5iuuuKLhjh074s4777y1HTp0yAIoV66cX3DBBesB2rZtu3ncuHGHAEydOrXy+++/vwCgf//+a+688856AB06dNj4ySefJC9cuLDCjTfeuOyFF16o9cUXX2xq1arV5sixTjnllHWVKlXySpUqZVevXn3HL7/8ktC4ceOdzYTS0tK2LVmypELfvn3rn3nmmevPOeecDdFlnTZtWsX69etvS0tL2w5wwQUXrHn++edrFTT/3Vm7dm3cihUryvfp02cdQGJiogO+bds2u/baa+t99913SXFxcfz222/lf/nllwSAunXrbotcq9atW29ZtGjRPvWR+OCDD5JnzJiR2KpVq3SArVu3xh166KHZAC+99FL14cOH18zOzraVK1eWmz59esVjjjkma1/yP/fcc9cmJCSwfv36uKlTpyb16NFjZzC7fft221PavBRUiIiIiBSyPdUsJCcn5+5p++GHH569vzUTeQ0YMKDBNddcs7x3797rx4wZk3zXXXfVAbjvvvuWn3322evffvvtKp06dUobO3bsvD3lc/rpp2/64osv5rz55ptVLr300kYDBgxYMWDAgNUJCQkeFxe0pk9ISCA7O3uPN6KdO3fe9OSTT9ZasWJF+UceeeTXRx999LBPPvkk+bjjjtsU2Se61iA+Pn6XPGvVqpUzY8aMWf/5z38OeeaZZ2qNGjWq+htvvLGooNdkb/nvq2HDhlVfvXp1wo8//vhThQoVvG7dui2ysrLiAMqXLx99LI+sLyh3tx49eqx+8sknf41eP3v27PJPPPFE7cmTJ/9Uq1atnO7duzfcunVrvnmb/e/0srKyfneuSUlJuQA5OTkkJydnR2pn9of6VBQSi/7ERERERIqBjRs3xjdo0GAHwPDhw3eOYDRz5swKmZmZWffee+/yli1bbp4xY0bFKlWq5GzatCk+v3zmzp1bvl69ejuuv/76VX369Fk5ZcqUxD0dt3Xr1puff/75ahDcdLdr124TwAknnLB5ypQpSXFxcZ6YmOjNmjXb8vLLL9fq0qXLxoKe07JlyxJycnK4+OKL191///2//vjjj78rS8uWLbcuWbKkwpw5c8oDjBo1ap9GtNqdatWq5R522GHbX3nllaoQ3KBv3Lgxbv369fE1a9bcUaFCBX/33XeTly5dWn5veSUlJeWsX78+32sd7bTTTtswZsyYar/++msCwIoVK+Lnzp1bfu3atfGVKlXKrV69es6SJUsSxo8fXyWSpnLlyjnr16/feY9fo0aNHVOmTKmYk5PD22+/XS2/41SvXj23Xr162//5z39WA8jNzeXbb7+tlN++u6Og4gCZWWcAd9eENyIiIhIzW7dujatdu3bLyOuOO+6ofeutty7905/+1LhZs2bpNWrU2NnG/qGHHjo00tG5XLlyft55563PzMzMio+P99TU1F06an/44YfJ6enpzdLT0zPefPPN6jfddNOKPZXlmWee+e8rr7xSMyUlJeO1116r8dRTTy0BqFSpkh922GHb27VrtxmgU6dOmzZv3hyXmZlZ4GY7ixYtKtexY8fUtLS0jIsuuujIu+6665fo7UlJSf7II48sPu2005o2a9YsPSkpKSc5OTmnoPnvyYgRIxY++eSTh6akpGS0a9cubcmSJQmXX375munTp1dOSUnJeOmll2o0atRo697y6dOnz6qrrrrqiLwdtfNq27bt1sGDB/960kknpaSkpGR06dIlZcmSJeXat2+f1bx58y2NGzdufv755x/Ztm3bnTU9ffv2XXXaaaft7Kh95513/nrWWWc1adOmTVrt2rV328zrtdde+/nFF1+smZqamtG0adNmb775ZtV9uTame+EDY2bDgZ+BWe4+2szi3L1AQ3jVSjvSz33unoNaPhERKRtKypCyZjbZ3dvFuhyFbfr06YtatWq1KtblkMD69evjqlSpkpubm0ufPn0aNG3adOvf//7332JdrpJu+vTpNVu1atUwv22qqdgPZhYf1dxpAPABcI+ZpRc0oBARERGRg+Oxxx6rGRn+dsOGDfEDBw5UwHeQqaaikJjZrUBb4M/uvnoP+/UD+gE0qFa57c+DexZRCaU0iB/4QqyLICJyQFRTIVJyqabiAJlZnJnFRS2fZ2Yfmdk9ZpYM4O73AuWBM8N98m0f5+7Puns7d29Xq/I+zSkiIiIiIlIsKajYi0gfCXfPNbPDzawR0A14GDgauMzMDgt3fx64FtRxW0RERETKDgUVexEGExXN7BFgEvAUMM/dPwIeAlqGL9z9LeBXM/tDrMorIiIiIlLUFFTkYWbxeZbbA/9HcK0aAeOA7gDu/gnwC3C8mTUNk3wBbEJEREREpIzQjNp5uHsOgJmdBOQQBA2NgCnuvt3MHiMIIm529weBkcD9wGHAHmejFBERkbLhii9fbVuY+Q3r1Gvy3vaJj49v27Rp053zPbz99tvzU1NTt+/LcV555ZWqGRkZW9u2bbvXuRb21Zw5c8qfccYZTefNmzezsPPenW+++abSkiVLyvfs2XN9UR2zrCrzQYWZ1QFquPuP4XI1YDhQFXjI3eeb2ftAHTNr6u7zzOwfwD/NbIS7zzKzq919cZjlMKBC0Z+JiIiIlGUVKlTInT179qwDyeOtt96qmp2dvX5fgoodO3ZQrly5AznsQbFjxw4mTZqUOGnSpMoKKg4+NX+CTsANUcsnAr+4+wnuPjZcNxI4FDjGzCq5+1fA+0A6QCSgMDNz93XuvsdZJkVERESKwpdffpl49NFHpzZr1iy9Y8eOTRcvXlwOYMiQITWbN2+enpqamnHqqac23rhxY9zHH39cedy4cVUHDx5cLy0tLWPmzJkVMjMzU7/44otEgGXLliXUrVu3BcDQoUNrdOnSpcmxxx6b0qFDh9QNGzbE9ejRo2GLFi3S09PTM0aMGFF1T+UaOnRoja5duzbu0KFD07p167a47777at1xxx2109PTM1q1apW2YsWKeIDMzMzUSy65pH5kzonPPvssEWDFihXxXbt2bZySkpLRqlWrtAkTJlQCGDhwYJ2zzz67UZs2bdLOPffcRvfff3+dd999t1paWlrGc889V+2zzz5LPOqoo9LS09MzWrdunTZ9+vQKkfKccsopjTt16tT0iCOOaH7llVfWi5R19OjRh2RkZKSnpqZmtG/fPgVgX8+3LCjzNRUEs2HPNbMq7r4eOAKoDmBm5YEcd19mZh8DZwBzgO+Ba/OO8KQRn0RERCRWtm3bFpeWlpYBUL9+/W1jxoz5+eqrr24wduzY+XXq1Ml+7rnnqt1www1133jjjUW9e/dee/31168CuPrqq+sMHTq05q233vpb165d151xxhnrL7nkkrV7O97MmTMTf/jhh5m1a9fOGTBgQN3OnTtveOONNxatWrUqvl27dundunXbcMghh+x2UuC5c+dWmj59+qysrKy41NTU5rfddtuvP/3006zLLrus/rBhw2rcfvvtvwFkZWXFzZ49e9b777+f1K9fv0bz5s2bedNNN9Vp1arVlnHjxi145513kvv27dsoUkszb968ihMmTJidlJTkQ4cOrTFp0qTKL7/88n8B1qxZE/f999/PLleuHG+99VbyTTfdVO/DDz9cADBr1qzE6dOnz6pUqVJukyZNmt9www0rEhMTfcCAAQ3Hjx8/Oy0tbXsk2LnlllsO39fzLe0UVEA5gknrIp2rFwG1zKxFVJOolsC/gJrAQggCiLBmYv8DidoNNZmZiIiIFIq8zZ++//77ivPmzavUpUuXFIDc3Fxq1aq1A2Dy5MmVbr/99robN26M37x5c/wJJ5ywz82DOnXqtKF27do5AOPHjz/kww8/rDp06NDDALZt22bz588v36ZNm902o+rQocPGatWq5VarVi03KSkpp0ePHusAWrRoseWHH35IjOzXq1evNQCnn376pk2bNsWtWrUqfuLEiclvvvnmfIBu3bpt7NevX8KaNWviAE477bR1SUlJ+d6frVmzJr5nz56NFi1aVNHMfMeOHTvnFevYseOGGjVq5AA0adJk64IFCyqsXr06PjMzc2NaWtp2gAM539KuzAQVZhYf6YQdzd2/MbO6wJXAk8BcoAXwhJndCAwAagAXuftjedKqZkJERESKJXe3Jk2aZE2bNm123m39+vVrNHr06Pnt27fPGjp0aI3PP/88Ob88EhISPCcnuH3asmXL7yb2TUxM3PlU3t0ZPXr0/FatWm0raPnKly+/8z4qLi6OihUreuR9dnb2zmPlnU94N/ML71S5cuXd1hbcfPPNdU844YSNH3/88YI5c+aU79KlS2p+5YmPj/9dwJHX/pxvaVfq+1SY2RHwv1GdotZb1PCxfwPuCAOPWcA/gLeAy4CF7v5Hd18TSVdkhRcRERHZTy1btty6Zs2ahHHjxlWG4Gn6pEmTKgJs2bIlrkGDBju2bdtmI0eOrB5Jk5SUlLNhw4ad94f169ffNnHixMoA//rXv6rt7lidO3feMGTIkNq5ucH9/Ndff12psM7jtddeqwbw4YcfJiUnJ+fUqFEj55hjjtn44osv1gAYM2ZMcrVq1bKrV6++SzBxyCGH5GzatGnn+WzYsCG+Xr162wGGDRtWc2/HPvHEEzdPnDgxefbs2eUh6MsBB/d8S6pSW1NhZhWB64AKZnYncCHQEfjW3YdDEGiETZg+NbNPgSFmdre7rwYeNbMEd88O84t39xzVToiIiMjeFGQI2IOtYsWKPnLkyAVXX311g40bN8bn5ORY//79V7Rr127roEGDlmZmZqZXr149u02bNps2bdoUD9C7d+81/fv3b/jMM8/UHj169IJBgwat6Nmz55HDhw+vdfLJJ6/b3bEeeOCBpf369WuQlpaWkZuba/Xr19/22WefzS+s80hPT8/Izs62Z599diHAgw8+uLR3794NU1JSMipVqpQ7fPjwhfmlPf300zc+/PDDh6elpWVcf/31y26++ebll19+eaMHH3ywzp7OJ6JOnTrZQ4cOXXTOOec0yc3NpUaNGju++eabeQfzfEsqK233yGZWGch19ywzO4sgkCgPNCAYxelp4Hx3HxfuHx8GFzWAOwj6Vrzu7lPD7QYHp6lTu3btfNKkSYWdrYiISLFlZpPdvV2sy1HYpk+fvqhVq1arYl2O0iYzMzP14YcfXnL88cdviXVZBKZPn16zVatWDfPbVmqaP5lZXNic6TTg+HD1OmA90Aa4391HAbcCN5hZIuysrYgLayduAaYBZ4Y1HXioSE9GRERERKQEKRVBhZm1B94I+01UBB4ys4Xh+/eA/wJHAbj700A8cHEkvbvnhv9uDAOP5929zPbeFxERESkOJk6cOEe1FCVDiQ4qwtoJA6YDDcysK5AD1AJGu/uH7j4FmAAcaWatw6RDgIFmVj2/fN19aREUX0RERESkVCjRQYW754ZNk6oDnwGD3H0k0BOobGadw13fJzjXzmZWwd0/ALpFRnQSEREREZH9V6KCCjOLN7NzzeyocNnM7CHgA4IaioZmNsDdvwSWE/SvwN3nAUuBqgS1GIRDx4qIiIiIyAEqaUPKJgO/ufs0MysHOFAPONHdV5nZV8ADZjYSGAP81czeJuiw/TgwLTJErIiIiIiIFI4SFVS4+zozm2hmNwMLgHHACUC5cJcPgb7ADe4+yMweBf4MDIvUTITzUmg0JxERETloch65rG1h5hc/8IW9znsRHx/ftmnTplmR5bfffnt+amrq9n05ziuvvFI1IyNja9u2bQt9wJp69eq1GDt27NzoWagvvfTS+ocffviOe++9d3lB8ujZs+cRN91004qDUT45MMW6+VNkxmszSzCzRDN7GMglGNWpLbADGAXcCxDWQqwArjSz1u4+w92vcfdZB3O+CREREZFYq1ChQu7s2bNnRV77GlAAvPXWW1V/+OGHfZodeseOHQXa7+yzz17z8ssv7xwkJycnh7Fjx1br27dvgfq4ZmdnM2rUqMUKKIqnYhlURAUAOWEzp0ruvoVgIrtuwHCCplBnAfcQdMC+1sweIwg4ekUmrwvzi1MwISIiImXNl19+mXj00UenNmvWLL1jx45NFy9eXA5gyJAhNZs3b56empqaceqppzbeuHFj3Mcff1x53LhxVQcPHlwvLS0tY+bMmRUyMzNTv/jii0SAZcuWJdStW7cFwNChQ2t06dKlybHHHpvSoUOH1A0bNsT16NGjYYsWLdLT09MzRowYUTVvWfr06bPmrbfe2hlUvP/++8l169bdnpKSsr1r166NmzVrlt6kSZNmDz/8cM3IPomJia3//Oc/10tNTc345JNPkqLL07t37wbNmzdPb9KkSbPrrruuTiRN3bp1W1x33XV1MjIy0lNSUjKmTp1aEWD9+vVx5513XsOUlJSMlJSUjOHDh1cF+Pe//33IUUcdlZaRkZF++umnH7l+/fpieX9c3BXLi5YnAPgIeN/MGhEEEJnuvhj4gaC2ohzwR2AbQUBxk7u/lye/3CIpuIiIiEiMbNu2LS4tLS0jLS0t4+STT268bds2u/rqqxu8/fbbC2bOnPlT3759V91www11AXr37r12xowZP82ZM2dWampq1tChQ2uefPLJm7t27brunnvu+WX27NmzmjVrtm1Px5s5c2bi22+/veD777+fc8sttxzeuXPnDT/++ONPX3755ZzBgwfX27Bhw+/uMzMzM7Pi4uL49ttvKwG8+uqr1c4777zVAP/6178WzZw586dp06bNGjZsWO3ly5fHA2RlZcUdc8wxm+fMmTPr1FNP3RSd3yOPPPLrjBkzfpo9e/bMr7/+OnnChAk7a1hq1qyZPWvWrJ8uvfTSlQ888EBtgEGDBh1+yCGH5MydO3fW3LlzZ/3xj3/cuGzZsoT77rvv8C+++GLurFmzfmrTps2Wu+++u3ZhfB5lTbHsU2FmRwAXuPuDwDvAMcCVwBZguZlVAj4GWgKXAg9Hj+YU1kwokBAREZEyI9L8KbL8/fffV5w3b16lLl26pADk5uZSq1atHQCTJ0+udPvtt9fduHFj/ObNm+NPOOGE9ft6vE6dOm2oXbt2DsD48eMP+fDDD6sOHTr0MIBt27bZ/Pnzy7dp0+Z3TZXOPffc1SNGjKjerl27Xz/66KNqDz744FKABx98sPbYsWOrAixfvrzczJkzKx522GGb4+Pjufjii9fmd/yXXnqp+vDhw2tmZ2fbypUry02fPr3iMccckwXQq1evtQCZmZlb3nnnnWoAX3zxxSEjR478OZK+Vq1aOa+99lqVBQsWVMzMzEwD2LFjh7Vt23ZTfseTPYtpUGFmyUBX4Ht3/yVq0xaCfhETgA3ADIIg4mWCoWFfc/eFZjYOWOzuO6LyVEAhIiIiZZ67W5MmTbKmTZs2O++2fv36NRo9evT89u3bZw0dOrTG559/npxfHgkJCZ6TkwPAli1bLHpbYmLizvstd2f06NHzozth56dPnz5rTzvttKadO3femJqauqV+/frZY8aMSf7888+TJ02aNDs5OTk3MzMzNSsrKw6gfPnyuQkJu96uzp49u/wTTzxRe/LkyT/VqlUrp3v37g23bt26s2akYsWKHil/dna27ZJBVLk7duy44d133124p3LL3sW6+VNj4DKgtZnFQdA5291XAgOAcwiaNHUDJgHDgOOA/gDu/lZ034lwnQIKERERKfNatmy5dc2aNQnjxo2rDEHtwaRJkyoCbNmyJa5BgwY7tm3bZiNHjtzZzyEpKSknutlS/fr1t02cOLEywL/+9a9quztW586dNwwZMqR2bm5wG/b111/n29m7WbNm26pVq5Y9ePDgeueff/4agHXr1sVXqVIlJzk5OXfq1KkVp0+fXnlv57Z27dr4SpUq5VavXj1nyZIlCePHj6+ytzQnnHDChkcfffTQyPLKlSvjTzzxxM2TJk1KmjFjRgWADRs2xP3www8V9paX7KrIayrC4MFD08zsc+B4YA4wl2B0J9x9bNhhux3BXBSnAE8BC4Evi7rcIiIiIgVVkCFgD7aKFSv6yJEjF1x99dUNNm7cGJ+Tk2P9+/df0a5du62DBg1ampmZmV69evXsNm3abNq0aVM8QO/evdf079+/4TPPPFN79OjRCwYNGrSiZ8+eRw4fPrzWySefvG53x3rggQeW9uvXr0FaWlpGbm6u1a9ff9tnn302P799zzvvvDX33ntvvQsvvHAdQPfu3dc/++yztY488shmRx555NZWrVpt3tu5tW/fPqt58+ZbGjdu3Pzwww/fXpAmS/fff/+ySy65pEHTpk2bxcXF+S233LK0b9++64YNG7boggsuOHL79u0G8Pe///3Xli1b7rHGRXZlRTkoUnTTJDOr4u7rzexw4AHgU+B1d8/Ks18aMB0Y4O7PReVV4uebaNeunU+aNCnWxRARESkyZjbZ3dvFuhyFbfr06YtatWq1KtblEDmYpk+fXrNVq1YN89tWpM2f3D3XzGqY2TDgaTM71d2XAe8SDBfbLLIf7GwKNRtoHR1QhPuU6IBCRERERKS0OKhBhZk1MLPKUcuZwOvAZOA14BYz6wG8CWwHjjOzne363D0n/DcyG3as+4CIiIiIiEgeB61PRdgf4iJgRjgEbAWCGok/A0nAI+HxuwLfAy8BfwNmAx/ml6c6YYuIiEgxlZubm2txcXFqSSGlUm5urhH2fc5PoT/5N7N42Nk8aSnwCjAQWObuawiGiL0NuA84A2gO9Hb3icA4IN9OPSIiIiLF2IyVK1dWCW+8REqV3NxcW7lyZRWCaR7yVWg1FZGO0+6eY2YV3X0rwUhNXwOfu/tH4a5VgBPdvUeYbjtwqJkd7u5PFlZ5RERERIpKdnb25cuXL39++fLlzYn9kP0ihS0XmJGdnX357nYotKAi0nHazK4C+prZ68ACoBfwHzP7Epjg7gvM7Esz+whoCAwH/hGZwK40jOokIiIiZUvbtm1/I5hXS6RM2u+gIuwzYdH9HMysN9CSYNK63gRNnFoAXwE9gQlm1gy4kKAvxQJ3nxmmjXP33LIUUCzetIYrvnw11sUQEZEYG9apV6yLICJyQParei4MADwcIjYpalNb4D3grwTR+vXuvp2gU3YN4P1wew13f8fdZ5pZXFg7oU7YIiIiIiIl0H7VVITBRBxwD9DRzEYTDBU7j2B42AHu3gHAzFoAiwkCjWbu/nXevA6g/CIiIiIiEmP7W1NxPPAWwdwStxP0jXiUYGjY9wg6aGNmlwKPA+nuvi4SUJjZQRvKVkREREREitb+jk5Qg2A42NfdfTzwMPAb0IoguLjZzMYB3YFB7j4hOrG7Z+93iUVEREREpFjZ3+ZP/zGz94BLgBuBFcBqINndPzGzr4BG7j4bNKKTiIiIiEhpdiDjKN8KnGdmHd09B2gNZAG4+7aogCK+tAYUZpZsZqeG/UYiI2KJiIiIiJQp+x1UuPt04ANglJm9RBBQvJnPfjn7X7xirwZQB/inmR0R68KIiIiIiMTCgXaYvgNIIZgx+59Q+ps6haNehSPq+iLgRTM7FLgG+Jhg2Nw9pe8H9ANoUK0yT33/yUEusYhI4Ykf+EKsiyAiIsXQAU0j7+4rCIaSvQ5Kf0ABwRC47u5mdnjU6v8jmD38D+HkfntK/6y7t3P3drUqVzyoZRURERERKQqFMbTrcCAyb0WpDCjCfiE54ftKwBPAcWGzr7fdfVY42tVhQBdgZuxKKyIiIiJStA6opgJ2dsp+LvIEvzAKVVxEOl5HBRQnAKcDU4EzgYoEzZ5w9znAEqCKmVWISYFFRERERGLggIOK0igqmPBw+UQz+xIYDAwFlrv7PIKmX9XN7Nww6TSgq7tvK/pSi4iIiIjEhoKKPKKHwDWzODP7A/BP4D53Pxn4Gmgazgo+DxgLXGZmldx9IvCLmXWOVflFRERERIqagoo83D3HzKqY2UNAOjAfWAdkhLs8BJwCtHb37cB44GegiZmVJxhWd0ZRl1tEREREJFbKfFBhZq3N7NSo5T8CnwGVCPpILAIeBDqaWU13nwxMAK4xs+RwWNmr3f3HMMiY5O4ri/o8RERERERipcwHFUAvggAioiUwxN2vcvcNYaDwBfBfoH+4zxDgFyAXgr4X4ehXuPuSIiu5iIiIiEgxUBhDypZIZhbn7rnAj8BZwFvhpjOAYeE+h7j7BmA18A5wj5mNdvefgEHR+YV57ZvaDTWRlIiIiIiUeGW2piIqCJgLrDaz9HD5GeDacJ8NZlYZOAaYDFwbBhTAztm1RURERETKtDJxUxyO1LQ7a4HKBB2tzd1fAdaY2fNmNhiYCJwErHf3CdEJ96t2QkRERESklCnVQYWZtTGzJHfPDper5d0nnLRuMXAc0DZcfSHwb6A60Mvd7yhtE/uJiIiIiBSWUhtUhBPY/Q24N1weCbxtZmeaWZVwXaQG42VgG9DbzOq6+3J3f8/dB7r7dAuU2mslIiIiInIgSt2Nspk1gp2zYd8OHB0GFB8DzxJ0yu4W7pMdNnlaCjwNrAIejq7RCDt0u5o6iYiIiIjkr1QFFWZWCXjEzOqa2clALeB14Dh3f8HdRxDMMdHazCKT2UWGgl3u7vcC3xEEIjXD9QomRERERET2oFQEFWYWD+DuWQS1DfOBv4TvXwY2mdlZ4e7fAFn8r7YiJ8wjci2GAp+4+6oiOwERERERkRKsRAcVUcFEjpklm1k9gknq1gPPuvssd18D3AfcFO47E1gAJJtZ7UhekRqJsKlTThGfioiIiIhIiVWig4qoWoa+wKfAde5+N3AZwXwTESMJ5qK4O1x+Axjs7iuKsrwiIiIiIqVRiQoqwhGdopfrm9nnwNHAm8AlZtbe3ccCv5rZzeGujQlqK5LDPDa5u2tEJxERERGRA7enSeGKlXAUprydpqsAa9x9QLhPFaAf8C1Bn4p3zKwzsAXo6+7XRidWJ2wRERERkQNX7IOKcMhXd/dcM0sCbiMYwekLIAlYb2Z1wmFh/wHMMbML3X2EmfUGqrn7O1H55ReciIiIiIjIfiq2zX8iTZ0iM1mb2XEE80zUBboATwETgUOBY8PgYw0wDTg9TPtlJKCI6tStgEJEREREpBAVu6AiMnt1JJgI1/0ReBGY4+4XAgMJ+kmcRNBX4lxghJl9STDPxDFhELKTRnQSERERETk4il3zpzCYcDNrDJwDvOHuY83sDOAQM6vk7llmdh9wv7u3M7MZwAUEzaLmAUcSDC0rIiIiIiIHWcxrKsKaiTOjZrjGzG4hGAa2HHCHmf2ZYFK6o4CmAO7+JrDdzO5093XA8wTNnr4CZrj7kiI9ERERERGRMirmQQXBCE6XAceZWVLY96Giux8NzCQYLraqu/8E/ABcFI7yBHA+8AiAu2cDvwAnufv9RX0SIiIiIiJlVUybP5lZvLuvM7MRwCnAFGA2cKGZdQFWA39292/DYOMh4EOCWozJ7v5LmE+Cu2e7+8uxORMRERERkbKrSGsqzCzZzM4xs3rhKgdw99HAVuBkgkDndWCLu58VBhQNgRvdfRlwrrtPjs43rKUQEREREZEYKOrmT40Jmjq1jswXERnqlSCQyACOAV4CapvZzWZ2P/ABwQR2uPv8Ii6ziIiIiIjswUEPKswsLmrOiWnA58DxQJNwl9xw21fAwnDbEuBCYAVQCejs7kMPdllFRERERGTfHdSgIlIb4e4e1bl6BMGEde3D4WE9qrbiJSAN6A7Mdvfh7n6tuy+LDk5ERERERKT4OKhBRdi8qYaZDQOeNrNTw34R7wIdgWbhfjnhjNg/A2MJJrnbEcknOjg5mOUVEREREZF9V6ijP5lZA2C1u28OlzOB+4FRwDLgFjM7BBgNdCYYRvZnd19DEODkuPuLefN199zCLKeIiIiIiBSeQgsqwqZJFwEzzKwSUIGgRuLPQBLBfBIJQFfge4KmTn8jGEL2Q3fPKayyiIiIiIhI0TngoCKcayIn7BuxFHiFIFAY7O5rzCwOeBC4D5gKjAF6u/u9ZjYOKLOjOS3etIYrvnw11sWQ0LBOvWJdBBEREZESab+DirAPhIf9ISq6+1aC0Zu+Bj5394/CXasAJ7p7jzDdduBQMzvc3Z880BMQEREREZHY2u+O2pFO02Z2FfCVmd0E1AB6AaeZ2XHhTNcLgC/N7CMzmwt8DNwQdthGIzqJiIiIiJRsBQ4qLBCXZ11voCVwTpjXfcBm4CugZ7hPM4I5J54AznH3+9x9RyQvjegkIiIiIlKyFSioCId09XCI2KSoTW2B94C/At2A6919O0Gn7BrA++H2Gu7+jrvPjMw3oRGdRERERERKhwL1qQiDiTjgHqCjmY0GXgfmAW8CA9y9A4CZtQAWEwQazdz967x5FWL5i41IH5NYl0NEREREpKgVtKbieOAtYDtwO9AQeJRgaNj3CDpoY2aXAo8D6e6+LhJQmFmhzodRnJhZi0hNTqzLIiIiIiISCwXtU1EDOAN43d3HAw8DvwGtCIKLm8PhYbsDg9x9QnRid88utBIXP5cDD5lZWqwLIiIiIiISCwUKKtz9PwQ1EpeEq1YAq4Fkd/8EOJWgCdQf3f270j6iU54O6zcTNPcaaGYnxqRAIiIiIiIxZAVttWNmrQiaQF3k7l+Z2X+AD9x9WJ794kvz7NjRfSfCIXOzzawycB4w0N1b7SV9P6AfQINqldv+PLjnQS+ziJR88QNfiHURRAqFmU1293axLoeIFK4CDynr7tOBD4BRZvYSkEXQSTvvfqUuoAiH042HYAhcM+tkZi8AfwnXbXb3l4B1ZnbbnvJy92fdvZ27t6tVueLBL7yIiIiIyEG2r5Pf3QHMJpgxu5e7ryoLTZ2iZg5PMrMqwA3AN0AfMxsctfuNwJlmlhyTwoqIiIiIxMA+BRXuvoJgKNnroGwMoxoZAtfMrgVmAM8AE9z9BYJmTH3MrH64+zzgC6BNDIoqIiIiIhIT+1pTATAcGJp3du3SIjI5X9RyazMbBVQlmOBvCdDczA519ynAZwQ1OADZQBWCkbFERERERMqEfQ4M3H2buz/n7rmlrZYibOqUG/abSDGzlsAy4GRgjbv/ALwBLCUYPheC0Z/ON7O27r4RmA+kx6L8IiIiIiKxUCprG/aFmVUys2Ng58zhFc3sSYLgoYm7LweGAieFSaYDU4G2ZnaUu68DjnP3yeH2FwmaQImIiIiIlAllPqgg6P9waVSTp25ADXdv5e7/DtfdCTQ1s27uvp0gqFgFHAoQ1mAQvv/N3VcVXfFFRERERGJLQQUcDswNmzzFAbUJJrPDzMqZWfmwmdf9wD/CzumzgLvd/aPYFVtEREREpHhIiHUBioGfgAfM7HF3325mFYFtZpYc9pHAzI5091fM7CSgIbDQ3Tcf8JFrN9SEViIiIiJS4pWZmor8RqsKO2bPBL4Erg9XjwYygMFm1tzMHgTuNbMa7n6xuy8sulKLiIiIiBR/pT6oMLNK8L/5JqLWW9S614BOYY3EQmAIsJagyVN1oL+7rw7TlfprJiIiIiKyL0pt86ew4/WTwLfAK2bWBegKfODuX4R9KCKT931H0Pn6EeBsd/8W+NbMqoajO0UPN5ub7wFFRERERMqoUvfU3cwSzCwxDBYmAeea2Z+Bv4e73GVmvcP38QDuvsHdbwVqmFk/M8sI16+zQJyCCRERERGR/JWqoMLMqgJ/BGqGq94CNgLnAde5+y3A/wG3hLUU2WHQEB/u/2dgB/AnMysP4AEFFCIiIiIiu1EqgorIHBNhU6V2wONmthDoDDwOHAJUNrMEd/8PMAe4O0we5+45YfrZ7v4iwQR2CiRERERERAqgRAcVkVqGsKkTZlYOWAgcDzzp7m+6+/fARIJJ7RLDpHcC15pZ7UhAEc3df3b37KI5CxERERGRkq1EBxVh06QcM6tmZn8ChgEfAoOBKmbWLNz1IeAooH0YhEwHTnT3FTEpuIiIiIhIKVKigwoAM7sG+Bo4B2gLZAIvA42ANgDu/ivwBdAXiAwxOykW5RURERERKW1KzJCyYb+J6LklMLPDgZOA4919lZndSNBR+xuCTtpdzaw5UBe4Cch2901FXngRERERkVKsRNRUhEO6urvnmllTMzsu3LQRSAMOC5fHEAwTe4G7jwbeAWoDr7n7Unf/TZPXiYiIiIgULgv7OBd7ZlaBoLahF5AFjAKeAQYAOe7+QLjf64AB97v7lBgVt0CSk5O9bdu2v1t3/vnn85e//IUtW7bwhz/8YZc0F198MRdffDGrVq3ivPPO22V7//796dmzJ0uWLOGiiy7aZfv111/PmWeeyZw5c7jiiit22T548GC6du3KtGnTuPbaa3fZft9999GhQwe++eYbbrnlll22P/bYYxx11FGMGzeOe+65Z5ftw4YNIzU1lXfffZchQ4bssv2VV16hfv36jBo1iqeffnqX7aNHj6ZmzZoMHz6c4cOH77L9vffeIzExkaeeeorXX399l+3jx48H4OGHH2bMmDG/21apUiXef/99AO6++24++eST322vUaMGb775JgB/+9vf+Pbbb3+3vV69eowYMQKAa6+9lmnTpv1ue0pKCs8++ywA/fr1Y+7cub/bftRRR/HYY48BcOGFF/LLL7/8bnv79u25//77AejevTurV6/+3faTTjqJ2267DYDTTz+drKys320/44wzuOGGGwA48cQTyUvfPX33QN89ffc+2SVdYTOzye7e7qBkLiIxUyybP+WdbM7MjgBuAI5w9/SwSdM/CGbCngDcZGbbgE0Ew8euImjyNCW//EREREREpPAUu5qKcFK6yBCxTYHII7NbgFMIRm3KMrO7CDpdPwAcCVxIEFD0B54E/u3uY4u6/PuiXbt2PmmS+ouLiEjZoZoKkdKpWNRUmNlhQAV3X+zubmb1gUeBJGAbcD/wBlAFOB94iWBSuxHAH939ZTObBNQBhgCpwNxdjyQiIiIiIoUt5p2WzawicAVwX9TqAcAYdz+NoKP1tcByYDpwjJk1cveVwOsETZ0gCJD+Asx3947uPq+ITkFEREREpEyLeVDh7lsJhn9NMLMzw9U5QAMz+wr4Eejv7r8RzDVhBJ21cfcX3P298P0O4O/u/mgRn4KIiIiISJlW5M2fzCwNWBz2i4h39xyCpkqfAL2Bdwk6WacCl7v7jDDdee4+2sxeBRblydPCIWezi/JcRERERESkiGsqzKw68Dfg5nBVLoC7ZwEfAzvMrAcwFNgONDazJmY2HBhoZnXd/Ut3XxKdb6Rjt4iIiIiIFL0iCSqiJpxbR9C5urWZNQ07ZceH25YAbwJ9gVnAI0AX4FmCmo0O7v5rUZRXREREREQK7qA2fwqDCY/MERHOiD2FoJ/EAOCasPkT7p5tZt8AnYC73P1G4C0zS3L3TWF+keZSIiIiIiJSTBzUmgp3zw1rI442s8Fm1sLdVxMMD9vIzE6CIFgIk6wExhB02k4KJ63bZGZxYb8JBRQiIiIiIsVMoQcVZmZR78ub2VPAYwRDv95hZhe6+3TgU+BSAHfPCQMIB7509+vcfVN0DYf6TYiIiIiIFE+FFlREgok8N//VgK/c/TiCSexaALea2SHAyDDZX8J9PUyfHeYX8+FuRURERERk7wrlxj0ypGv4voeZ3Wxmx7n7CmC0mQ0BzgOOA2YAg919OfABUC06fUSklkJERERERIq3QumoHfabqAecDfyJYK6Jl83sRnf/t5lVBq5395Vmtgq4zsyGAa+U5WZNizet4YovXz2gPIZ16lVIpRERERER2T/7FVTsZhSm54CqQF93n2tmC4FBZvYhcATQzcxuAtYD3dx9QVR+caqZEBEREREpmfar+VMkoDCzS8zsdDMrD9wW5lfVzCq4+6hw+WSC4WNzgB0ETZ/ez5OfAgoRERERkRKqQDUVefs8mFlT4EVgITAJuA74A/ADQRDxC7AU+ALYFtZKDIlKr5oJEREREZFSYq9BRXRTJzMr7+7bgTTgHuBDghmyE4FKwOMEM2CnmtkOoH24Ljo/BRQiIiIiIqXIXps/hXNIJJjZA0Bk+NdM4FHgG2C2u3d0943u/gPBxHY1gffcPcPdF+bJr9QFFGZWNfxXw+CKiIiISJmz15tgM2sNfA1UIAgYAP4d/jvQ3e8O9/urmR1H0GG7HMG8FKX6RtvMKpnZVcB9ZlanNAZMIiIiIiJ7U5Ab/iRgGnA7QfeKFsAW4BngMTO70Mw+As4Blrv7BuA94FgzK1cab7Qt5O5ZwJPAz8CDZtYyxkUTERERESlyBemo/RtQC/gUmAI0BdYA5wO/Am2AF939tUgCd3+08ItaPOTtExK+f9jMBgMXmtmb7j4hdiUUERERESlaVpC558ysOpDr7uvMLBW4Ghjk7hvz7Bcf9sHYZYbski5vMGFmNxLMDj7N3ZeZWSOCif8qA3939+zd5NMP6AfQoFrltj8P7nnwCy9SgsQPfCHWRRCRg8jMJrt7u1iXQ0QKV0H7O6wDNpvZBcA/gWxgc2SjmRn8b/6K0hRQRDV1yg2Xq5vZXcApwBnAHeH2hQTNxCoCzXaXn7s/6+7t3L1drcoVi+AMREREREQOrgIFFeENdSbQnWDyumvyNAEqNUFEXh4ys+ZmNhK4n6DW5mTgYaA8cGW4+zTgUIKgS0RERESkTNiXkZm+c/ce7v4ZlPpRneKj3ieY2dUE83KMJZiP41gzSyaY5O8d4JRw9Kel/K+/iYiIiIhImVDgwCBqAry4cLlUjuoEvzvX+sBhwKlAnLu/AgwC4oEO7r4D+ApYRTCrOMBrwLLSHHSJiIiIiETb5xvfUhpM1IP/NeMys1PMbApwObAWGApUN7PUsDbiPYKRng5195UEEwE+Fma3Cfi0NF4nEREREZH8lPmn6WbWFrg4fG9m1hAYDNzm7n93983A98BnwF/DZI8BKUAnAHef5e6/hu9nuPvcojwHEREREZFYKvNBBZAFdIOdNRUdgVnuPtbMyodDya4B3gKOMLMzw/16u/ubMSu1iIiIiEgxUZDJ70qtcCjYWWY2xcxOc/cPgPnA3QDuvj1q94XA50DtcNv8qDz2b/Sr2g01Jr+IiIiIlHhlpqYi0gk7WjhU7CHAMqCemVV09++AiWb2gplVMLNOZjYGqAY87u7P582jSE5ARERERKSYKvVBRWR42Oib/+gAw903AEuAFvxv0rq/AtWBV4CHgFfdfUE42lO+AYqIiIiISFlVqps/mVlX4FKgVzjE62UETZjmA25m8eHwsaOB5gTzTax39/lm1gM43N2X5M1XtRMiIiIiIv9T2msqZgPNzWwAMBLoAzwA/AmC+SjCwGIDQa2EAXeE27IjAUX0ZHgiIiIiIvJ7pS6oMLNuZtY0XMwiGB52EDDc3TsBHwBtzaxDuI8DuPtUd7+PoAbjynBoWcJtOUV2AiIiIiIiJUypCCrMLMnM/hgubgM+NbPHgP8jmPF6PXB0uP1jYANwgplVcvfccH6KyLUYALxPEJCIiIiIiMhelIqggmAiugfDiezWAhWAlu5+YTjHxPXA2WaW7O4LgclhmlMg6CMRmQHb3de7+2J3XxGTMxERERERKWGsJPc5jp4jwsyeAxYA/waaAi+5e81wWzzwHLDW3a83s6rAicB4d18Xg6ITlmsjMCdWxy9jagKrYl2IMkLXuujoWhcdXevCc4S714p1IUSkcJW4oCLsL7HF3X8Nl9MJOldXALoAl7r7aDN7E9jg7peE+zUjaPp0orvPjUnh8zCzSe7eLtblKAt0rYuOrnXR0bUuOrrWIiJ7VhKbP3UmnNU6NAiY7e5nA88Afc0sEbgCuMDMjjWzO4FDgFOKS0AhIiIiIlJalLigwt2fBaI7ZmcDE8NtNwGVgSvdfRVBp+u/AzWAKe4+IwZFFhEREREp1Yr15HeRmauj+k00JShzU+BKYCyQA1QNO2FvBD4CBpnZWHd/wcxGufum2JzBXj0b6wKUIbrWRUfXuujoWhcdXWsRkT0oEX0qzKw8wcR0jxEM9zoGeI9gwro5wN+ATwgCimuAOsD17r4oTG8E55pb1GUXERERESntil3zp0jtRNTyAOBpgmZNs4Ezw+BgCHAjMAN4CmgFjAbmuXv3SEABvx8yVkREREREClexCyqimjo1ClflAucA9xAMC9vOzBq6+8fA98CT7v4JQf+JDu4+NEwfX+SF3w0zO83M5pjZfDMblM/2CmY2Ktw+IXo2b9k3BbjWx5vZFDPLNrPzYlHG0qIA13qgmc0ysx/M7BMzOyIW5SwNCnCtrzSzH81smpl9ZWYZsShnabC3ax21X3czczPTiFAiIhSDoMLM0sysUvjewn87Ak+Z2RnAW8DDBJPVXQL8ABwVJn8EiAvT57r7FjOLD+evyCnaM8lfGNw8CZwOZAB/yuc//MsI5tBoAjwKPFi0pSwdCnit/wtcDLxatKUrXQp4racC7dy9JUEt4kNFW8rSoYDX+lV3b+HuRxFc50eKtpSlQwGvNWaWTNDUdkLRllBEpPiKaVBhZtUJ+kPcHFkF4O5fAcOATgSzYc8kqKlIBLoB6eF+P7n7Je6eFQki3D3Hi1dHkUxgvrv/7O7bgZHAWXn2OQt4KXw/GjgpbzMwKZC9Xmt3X+TuPxDUgMn+K8i1/szdt4SL3wH1iriMpUVBrvWGqMXKQHH6G1iSFOTvNcDdBA9/thZl4UREirOYBBVmFjnuOmAE0NrMmrp7rplFRqQaQ9AR+wzgVmC7u/8j3H/LbvIrjuoCS6KWfwnX5buPu2cD6wmGwZV9U5BrLYVjX6/1ZQSDLMi+K9C1NrO/mtkCgpqKq4uobKXNXq+1mbUB6rv72KIsmIhIcVekN+NmFhc2TcoFCP+dAvxI0CciclMNQXOmGcBtBE84bw23X+Xu/xedrzphixRfZnYh0A74R6zLUpq5+5Pu3pig5ndwrMtTGoUPsB4hqEEXEZEoRRpUuHuuu7uZHW1mg82shbuvBt4AGpnZSbDzD7eHaV4H+hM2kbIoRVn2A/ArUD9quV64Lt99wpqaKsDqIild6VKQay2Fo0DX2sy6EjwQ6Obu24qobKXNvn6vRwJnH8wClWJ7u9bJQHNgvJktAo4F3lFnbRGRIggqom/+zay8mT1FMN/EKuAOM7vQ3acDnwKXwv9qHqImv3s7Mhu2RznYZS8k3wNNzaxRON/GBcA7efZ5B+gbvj8P+LQEnV9xUpBrLYVjr9fazFoT9I3q5u6/xaCMpUVBrnXTqMU/AvOKsHylyR6vtbuvd/ea7t7Q3RsS9BXq5u6TYlNcEZHi46AFFVEBQfTNcTXgK3c/DtgGtABuNbNDCJ6umZn9JZKuNNxYh825BgAfAj8Br7v7TDO7y8y6hbu9ANQws/nAQGC3wxjK7hXkWoe1ZL8APYBhZjYzdiUuuQr4vf4HkAS8EQ51qgBvPxTwWg8ws5lmNo3gb0jf/HOTPSngtRYRkXwclBm1w34TkfkmegBHEgQTX4dPf+4H0giG9nwKWOjuN5lZH4Kq5/tKQ0AhIiIiIlIWHJSairDfRD0LZsO+lmCo2JfN7NxwmL7KwPXuvpKgGdR1ZtYYeMXd71VAISIiIiJSciTsfZe9M7P4fCabew6oCvR197lmthAYZGYfAkcA3czsJoLhU7u5+4Ko/OI0opOIiIiISMlQKDUVkYDCzC4xs9PDJk63hflXNbMK7j4qXD6ZoM1qDrADGOzu7+fJTwGFiIiIiEgJsV81FdF9JsLlpsCLwEJgEnAd8AfgB4Ig4hdgKfAFsC2slRgSlV41EyIiIiIiJdQ+d9SObupkZuXdfbuZnUlQ6/AhwYzXRwCnA42AZ4G54fb2wB/dfWFUfgooRERERERKsH1u/uTuOWaWYGYPAH8JV2cCjwLfALPdvaO7b3T3HwgmtqsJvOfuGdEBRZifAgqRImZmm4r4eA3NrFdRHjOfMjxjZsdFLZ9gZt/m2SfBzFaYWZ0C5lnHzEYXdlkLcNyGZpYVDiGLmdUys6/MbIaZnR2139vR52Jm/zCz5WZ2Q1GXWURESrd9DirCCa2+BioQBAwA/w7/Hejud4f7/TX8D/w5oBzBvBSR2bJFpIwIZ4lvCMQ0qCCY/fi7qOUvgXpmdkTUuq7ATHdfurfMzCzB3Ze6+3mFXM6CWuDuR4Xv/wQ8Q/CA51qAsAZ5avS5uPuN4X4iIiKFan9u8JOAacDtBN0rWgBbCP6jeszMLjSzj4BzgOXuvgF4DzjWzMqpZkKk+DCzE83s8/CJ9s9m9oCZ9TaziWb2YzjUM2Y2PHzSP8nM5prZGeH6imb2YrjvVDPrHK6/2MzeMbNPgU+AB4BO4SR414VP2r80synhq0NUecab2Wgzm21m/4pMpBlOXPiNmU0Py5dsZvHh0/fvzewHM7tiN+eZDsyNHqUu/Fv0OsGsyREXAK+ZWaaZfRue0zdmlprfeYXnMSPcVqTnlMcOIJHgYU9OGMhdCzxUgLQiIiIHbH86av8G1AI+BaYATYE1wPnAr0Ab4EV3fy2SwN0fPfCiishB0gpIJ/gd/ww87+6ZZnYNcBXhk2+C2oZMoDHwmZk1Af5KMDVNCzNLAz4ys5Rw/zZAS3dfY2YnAje4eyQYSQROdvetFgz08BrQLkzXGmhGMLjD18BxZjYRGAX0dPfvzewQIAu4DFjv7kebWQXgazP7KG8zS4I+Xh/kc+6vEdSmPhim/wPBjNTZQCd3zzazrsB9QPd8zqthVF6/FfE5RXs1fPUDbiZomvqKu2/ZQxoREZFCs89BhbvPMbPLgVx3Xxc+wbsaqOzuo4Gd7Yst7NRt9vvRokSkWPne3ZcBmNkC4KNw/Y9A56j9Xg+f7s8zs5+BNKAj8DiAu882s8VAJKj42N3X7OaY5YAnzOwoguGlU6K2TXT3X8LyTCMIZtYDy9z9+/BYG8LtpwAtzSzSBKkKwYOOvDfgpwKX5C2Eu08ys6Tw71g6MCEMFuoDL4XBgYfljdjdeRX1OUWfx3rgj2H6asAg4Bwzew6oBgxx9293l15ERORA7e/kd+uAeDO7gOBJ5iRgc2RjJIiINDVQQCFSrG2Lep8btZzL7/9G5P0d7+13vXkP264DVhDUksQBW3dTnhz2/HfKgKvc/cPd7hDUilTdQz+J1wiaPaWH7wHuBj5z93PC2ojxUfvv7ryK7Jz24jbgXoJ+Fl8RPOj5N0FgJSIiclDsV6fp8GllJkFzgMHufk10XwkFESKlUg8ziwv7WRwJzCHo7NwbIGz21CBcn9dGIDlquQrBU/pc4CIgfi/HngMcbmZHh8dKDvsNfAj0N7NykTKYWeU8aTsDn+0h79eAC4EuwNtR5fs1fH/xXsoWUZTnlK+wZqWeu48n6GORSxD8VSrgOYiIiOyXAxmJ6Tt37+Hun4FGdRIpA/4LTATeB650963AU0Ccmf1I0D/gYnfflk/aHwg6EE83s+vCdH3NbDpBM6o91Wrg7tuBnsDjYZqPgYrA88AsYErYYXoYu9YC7K4/RSTvn8Ljf+rukXI8BNxvZlPzyW93ivKcdude4Nbw/WtAf+B74P8KmF5ERGS/7PPkd7tkoMnrREo9MxsOjAn7TZUoZjYFOMbdd8S6LIUlbJI1xt2b70faO4BN7v5wYZdLRETKrgOuXVBAISLFmbu3KU0BRSgHqBJ2+i4wM/sHQVOvPdaiiIiI7KsDrqkQEREREZGyTf0gRERERETkgCioEBERERGRA6KgQkREREREDoiCChEREREROSAKKkRERERE5IAoqBARERERkQPy/0mYzkDthaEdAAAAAElFTkSuQmCC\n", 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Datasets Size Comparison

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Verify Test dataset size comparing it to the Train dataset size.

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Accuracy1.00Accuracy1.00
Precision1.00Precision1.00
Recall1.00Recall1.00
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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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j6NChPXq/7zIyM2tS25/OpJ0vsmSTcYBe3cCW398rNdUaOnQobW1tPXqvA8HMrElDXnyasXec0DsDa+LJpn3Np4zMzAxwIJiZWeZAMDMzwIFgZmaZA8HMzAAHgpmZZQ4EMzMDHAhmZpaVGgiSDpA0V9I8ScfX6T5a0s2S/izpXkmTy6zHzMw6V1ogSBoEnAMcCEwEpkmaWNPbicDlEbELcDBwbln1mJlZY2UeIbwZmBcR8yPiReAyYGpNPwFsnF9vAjxaYj1mZtZAmYEwClhUaG7P7YpOBg6R1A5cBXym3oAkzZA0S9KsJUuWlFGrmdmAV/VF5WnAhRHRBkwGLpb0ipoi4ryImBQRk0aOHNnnRZqZDQRlBsJiYOtCc1tuV3Q4cDlARNwODAVGlFiTmZl1osxAuAuYIGmspHVJF41n1vTzCLAPgKTXkwLB54TMzCpQWiBExCrgaOBa4H7S3URzJJ0qaUru7TjgCEn3AJcC06NVf4rIzGwtV+oP5ETEVaSLxcV2JxVe3we8rcwazMysOVVfVDYzsxbhQDAzM8CBYGZmmQPBzMwAB4KZmWUOBDMzAxwIZmaWORDMzAxwIJiZWeZAMDMzwIFgZmaZA8HMzAAHgpmZZQ4EMzMDHAhmZpY5EMzMDHAgmJlZ5kAwMzPAgWBmZpkDwczMAAeCmZllDgQzMwMcCGZmljkQzMwMcCCYmVnmQDAzM8CBYGZmmQPBzMwAB4KZmWUOBDMzAxwIZmaWORDMzAwoORAkHSBprqR5ko7vpJ+PSLpP0hxJl5RZj5mZdW5wWQOWNAg4B9gPaAfukjQzIu4r9DMBOAF4W0Q8JWmLsuoxM7PGyjxCeDMwLyLmR8SLwGXA1Jp+jgDOiYinACLiiRLrMTOzBsoMhFHAokJze25XtB2wnaTfS7pD0gEl1mNmZg2UdsqoG58/AdgbaANuk/SGiHi62JOkGcAMgNGjR/dxiWZmA0OXRwiSNpS0Tn69naQpkoY0MezFwNaF5rbcrqgdmBkRL0XEAuBBUkCsISLOi4hJETFp5MiRTXy0mZl1VzOnjG4DhkoaBVwHfBy4sIn33QVMkDRW0rrAwcDMmn5+RTo6QNII0imk+c0UbmZmvauZQFBEPA98ADg3Ij4M7NDVmyJiFXA0cC1wP3B5RMyRdKqkKbm3a4Flku4Dbga+EBHLejIiZmb26jRzDUGS9gA+Bhye2w1qZuARcRVwVU27kwqvAzg2/5mZWYWaOUL4HOm7Ar/Me/jjSHvzZma2FunyCCEibgVulbRBbp4PfLbswszMrG81c5fRHvkc/wO5+Y2Szi29MjMz61PNnDI6C9gfWAYQEfcAe5VYk5mZVaCpbypHxKKaVqtLqMXMzCrUzF1GiyS9FYj8hbRjSLeRmpnZWqSZI4QjgaNIzyFaDOycm83MbC3SzF1GS0nfQTAzs7VYl4Eg6YdA1LaPiMNKqcjMzCrRzDWE3xZeDwXeDzxaTjlmZlaVZk4Z/bzYLOlS4H9Kq8jMzCrRkx/ImQD4py7NzNYyzVxDWEG6hqD8/3HgiyXXZWZmfayZU0bD+qIQMzOrVqeBIGnXRm+MiD/1fjlmZlaVRkcI32zQLYB39XItZmZWoU4DISLe2ZeFmJlZtZr5HgKSdgQmkr6HAEBEXFRWUWZm1veaucvoK8DepEC4CjiQ9D0EB4KZ2Vqkme8hfAjYB3g8Ij4JvBHYpNSqzMyszzUTCCsj4h/AKkkbA08AW5dblpmZ9bVGt52eA1wK3ClpOPAD4G7gWeD2PqnOzMz6TKNrCA8C3wC2Ap4jhcN+wMYRcW8f1GZmZn2o01NGEfFfEbEH6feTlwEXANcA75c0oY/qMzOzPtLlNYSIWBgRZ0bELsA04CDggbILMzOzvtVlIEgaLOl9kn4CXA3MBT5QemVmZtanGl1U3o90RDAZuBO4DJgREc/1UW1mZtaHGl1UPgG4BDguIp7qo3rMzKwijZ5l5IfXmZkNID35xTQzM1sLORDMzAxwIJiZWeZAMDMzoORAkHSApLmS5kk6vkF/H5QUkiaVWY+ZmXWutECQNAg4h/T7CROBaZIm1ulvGHAM8MeyajEzs66VeYTwZmBeRMyPiBdJX2ybWqe/04AzgZUl1mJmZl0oMxBGAYsKze253T9J2hXYOiKubDQgSTMkzZI0a8mSJb1fqZmZVXdRWdI6wLeA47rqNyLOi4hJETFp5MiR5RdnZjYAlRkIi1nzl9XacrsOw4AdgVskPQy8BZjpC8tmZtUoMxDuAiZIGitpXeBgYGZHx4hYHhEjImJMRIwB7gCmRMSsEmsyM7NOlBYIEbEKOBq4FrgfuDwi5kg6VdKUsj7XzMx6ptHTTl+1iLgKuKqm3Umd9Lt3mbWYmVlj/qaymZkBDgQzM8scCGZmBjgQzMwscyCYmRngQDAzs8yBYGZmgAPBzMwyB4KZmQEOBDMzyxwIZmYGOBDMzCxzIJiZGeBAMDOzzIFgZmaAA8HMzDIHgpmZAQ4EMzPLHAhmZgY4EMzMLHMgmJkZ4EAwM7PMgWBmZoADwczMMgeCmZkBDgQzM8scCGZmBjgQzMwscyCYmRngQDAzs8yBYGZmgAPBzMyyUgNB0gGS5kqaJ+n4Ot2PlXSfpHsl3ShpmzLrMTOzzpUWCJIGAecABwITgWmSJtb09mdgUkTsBPwM+HpZ9ZiZWWNlHiG8GZgXEfMj4kXgMmBqsYeIuDkins+NdwBtJdZjZmYNlBkIo4BFheb23K4zhwNXl1iPmZk1MLjqAgAkHQJMAt7RSfcZwAyA0aNH92FlZmYDR5lHCIuBrQvNbbndGiTtC3wZmBIRL9QbUEScFxGTImLSyJEjSynWzGygKzMQ7gImSBoraV3gYGBmsQdJuwDfJ4XBEyXWYmZmXSgtECJiFXA0cC1wP3B5RMyRdKqkKbm3bwAbAVdImi1pZieDMzOzkpV6DSEirgKuqml3UuH1vmV+vpmZNc/fVDYzM8CBYGZmmQPBzMwAB4KZmWUOBDMzAxwIZmaWORDMzAxwIJiZWeZAMDMzwIFgZmaZA8HMzAAHgpmZZQ4EMzMDHAhmZpY5EMzMDHAgmJlZ5kAwMzPAgWBmZpkDwczMAAeCmZllDgQzMwMcCGZmljkQzMwMcCCYmVnmQDAzM8CBYGZmmQPBzMwAB4KZmWUOBDMzAxwIZmaWORDMzAxwIJiZWeZAMDMzoORAkHSApLmS5kk6vk739ST9NHf/o6QxZdZjZmadKy0QJA0CzgEOBCYC0yRNrOntcOCpiBgPfBs4s6x6zMyssTKPEN4MzIuI+RHxInAZMLWmn6nAj/LrnwH7SFKJNZmZWScGlzjsUcCiQnM7sHtn/UTEKknLgc2BpcWeJM0AZuTGZyXNLaXiFiAYQc34l+oU529v8bzr3wbA/Numqx7KDIReExHnAedVXUdfkDQrIiZVXYd1n+dd/+b5V+4po8XA1oXmttyubj+SBgObAMtKrMnMzDpRZiDcBUyQNFbSusDBwMyafmYCn8ivPwTcFBFRYk1mZtaJ0k4Z5WsCRwPXAoOACyJijqRTgVkRMRM4H7hY0jzgSVJoDHQD4tTYWsrzrn8b8PNP3iE3MzPwN5XNzCxzIJiZGeBAMDOzzIFgZmaAA6ElSHqbpOslPShpvqQFkuZXXZfZQCTpdVXXUBXfZdQCJD0AfB64G1jd0T4i/CW9FiVpBVBv5REQEbFxH5dkvUTSIxExuuo6qtAvHl0xACyPiKurLsKaFxHDqq7Bek7SdzrrBAzvw1Jaio8QWoCkM0hf3vsF8EJH+4j4U2VFWUOSNmvUPSKe7KtarPvyEd5xFNa3gm9GxIg+LqklOBBagKSb67SOiHhXnxdjTZG0gHTKqN4jKyMixvVxSdYNkm4CToyIP9TptiAixlZQVuUcCGY24OQjvJUR8XzVtbQSX0NoAZI2Ab4C7JVb3QqcGhHLq6vKmiVpU2ACMLSjXUTcVl1F1oSNfFrvlXzbaWu4AFgBfCT/PQP8sNKKrCmSPgXcRnqI4yn5/8lV1mRN+VXHC0k/r7COluJAaA3bRsRX8s+Nzo+IUwCfg+4fjgF2AxZGxDuBXYCnK63ImlG89uN1LXMgtIa/S9qzo0HS24C/V1iPNW9lRKwEkLReRDwAbF9xTda16OT1gOZrCK3hX4Ef5WsJIv02xPRKK7JmtUsaTjoFcb2kp4CFlVZkzXijpGdI69v6+TUM8C8W+i6jFiJpY4CIeKarfq31SHoH6Wdgr4mIF6uux6y7HAgVknRIRPxY0rH1ukfEt/q6JuseSW8B5kTEity8MfD6iPhjtZWZdZ+vIVRrw/x/WCd/1vq+CzxbaH42tzPrd3yEYPYqSJodETvXtLs3InaqqCSzHvMRQguQ9HVJG0saIulGSUskHVJ1XdaU+ZI+m+fdEEnHAH50ufVLDoTW8O58Ifm9wMPAeOALlVZkzToSeCuwGGgHdgdmVFqRWQ/5ttPW0DEf3gNcERHLpXrPTLNWExFPAAdXXYdZb/ARQmv4bf6RnDcBN0oaCaysuCZrgqTt8mm+v+bmnSSdWHVdZj3hi8otIj99cXlErJa0AbBxRDxedV3WmKRbSaf3vh8Ru+R2f42IHautzKz7fMqoQpLeFRE3SfpAoV2xl1/0fVXWTRtExJ01821VVcWYvRoOhGq9A7gJeF+dboEDoT9YKmlb8vNwJH0IeKzaksx6xqeMzF4FSeOA80h3Gj0FLAA+FhF+npH1O76o3AIkfTU/IK2jeVNJp1dYkjUpP658X2Ak8DrSUd+ejd9l1pocCK3hwIh4uqMhIp4CJldXjnUlf5HwBElnS9oPeB74BDCP9CNHZv2OryG0hkH5WfovAEhaH1iv4pqssYtJp4huB44Avkx6dPL7I2J2hXWZ9ZgDoTX8hPT9g46fzfwk8KMK67GujYuINwBI+m/SheTRHT+WY9YfORBaQEScKekeYN/c6rSIuLbKmqxLL3W8yN8daXcYWH/nu4xahKRtgAkRcUP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" ] @@ -3077,23 +3256,23 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -3102,8 +3281,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 
Single unique value2Single unique value2
\n" @@ -3152,23 +3331,23 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -3189,12 +3368,12 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 
25.802.705.101.90225.802.705.101.902
\n" @@ -3234,23 +3413,23 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -3264,13 +3443,13 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 
petal width (cm)0.2029.000.002900.00petal width (cm)0.2029.000.002900.00
\n" @@ -3310,23 +3489,23 @@ "data": { "text/html": [ "\n", - "\n", + "
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target100%[2]target100%[2]
\n" @@ -3358,23 +3537,23 @@ "
\n", "

Other Checks That Weren't Displayed

\n", " \n", - "\n", + "
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Check
Trust Score ComparisonDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].Trust Score ComparisonDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].
Class Performance ImbalanceDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].Class Performance ImbalanceDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].
Trust Score ComparisonDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].Trust Score ComparisonDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].
Roc ReportDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].Roc ReportDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].
Confusion Matrix ReportDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].Confusion Matrix ReportDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].
Performance ReportDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].Performance ReportDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].
Calibration MetricDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].Calibration MetricDeepchecksValueError: Classification labels must be a consecutive set from 0 to MAX_LABEL, found [2].
Date Train Test Leakage OverlapDeepchecksValueError: Check DateTrainTestLeakageOverlap requires dataset to have a date columnDate Train Test Leakage OverlapDeepchecksValueError: Check DateTrainTestLeakageOverlap requires dataset to have a date column
Date Train Test Leakage DuplicatesDeepchecksValueError: Check DateTrainTestLeakageDuplicates requires dataset to have a date columnDate Train Test Leakage DuplicatesDeepchecksValueError: Check DateTrainTestLeakageDuplicates requires dataset to have a date column
Index Train Test LeakageDeepchecksValueError: Check IndexTrainTestLeakage requires dataset to have an index columnIndex Train Test LeakageDeepchecksValueError: Check IndexTrainTestLeakage requires dataset to have an index column
Boosting OverfitDeepchecksValueError: Unsupported model of type: RandomForestClassifierBoosting OverfitDeepchecksValueError: Unsupported model of type: RandomForestClassifier
Label Ambiguity - Test DatasetNothing foundLabel Ambiguity - Test DatasetNothing found
Rare Format Detection - Test DatasetNothing foundRare Format Detection - Test DatasetNothing found
Label Ambiguity - Train DatasetNothing foundLabel Ambiguity - Train DatasetNothing found
Special Characters - Test DatasetNothing foundSpecial Characters - Test DatasetNothing found
Special Characters - Train DatasetNothing foundSpecial Characters - Train DatasetNothing found
String Length Out Of Bounds - Test DatasetNothing foundString Length Out Of Bounds - Test DatasetNothing found
String Length Out Of Bounds - Train DatasetNothing foundString Length Out Of Bounds - Train DatasetNothing found
Rare Format Detection - Train DatasetNothing foundRare Format Detection - Train DatasetNothing found
Mixed Nulls - Test DatasetNothing foundMixed Nulls - Test DatasetNothing found
String Mismatch - Test DatasetNothing foundString Mismatch - Test DatasetNothing found
String Mismatch - Train DatasetNothing foundString Mismatch - Train DatasetNothing found
Mixed Types - Test DatasetNothing foundMixed Types - Test DatasetNothing found
Mixed Types - Train DatasetNothing foundMixed Types - Train DatasetNothing found
String Mismatch ComparisonNothing foundString Mismatch ComparisonNothing found
Mixed Nulls - Train DatasetNothing foundMixed Nulls - Train DatasetNothing found
Single Value in Column - Train DatasetNothing foundSingle Value in Column - Train DatasetNothing found
Train Test Samples MixNothing foundTrain Test Samples MixNothing found
Data Duplicates - Train DatasetNothing foundData Duplicates - Train DatasetNothing found
Category Mismatch Train TestNothing foundCategory Mismatch Train TestNothing found
\n", @@ -3610,11 +3789,18 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Simple Suite For Model Performance: 0%| | 0/5 [00:00\n", - "#T_176c3_ table {\n", + "#T_224aa_ table {\n", " text-align: left;\n", "}\n", - "#T_176c3_ thead {\n", + "#T_224aa_ thead {\n", " text-align: left;\n", "}\n", - "#T_176c3_ tbody {\n", + "#T_224aa_ tbody {\n", " text-align: left;\n", "}\n", - "#T_176c3_ th {\n", + "#T_224aa_ th {\n", " text-align: left;\n", "}\n", - "#T_176c3_ td {\n", + "#T_224aa_ td {\n", " text-align: left;\n", "}\n", "\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -3666,10 +3852,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Status
✓
Simple Model ComparisonRatio not less than 0.9 between the given model's result and the simple model's result
✓
Simple Model ComparisonRatio not less than 0.9 between the given model's result and the simple model's result
\n" @@ -3718,23 +3904,23 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -3744,99 +3930,94 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
Parameter
bootstrapTrueTrue
ccp_alpha0.000.00bootstrapTrueTrue
class_weightNoneNoneccp_alpha0.000.00
criterionginiginiclass_weightNoneNone
max_depthNoneNonecriterionginigini
max_featuresautoautomax_depthNoneNone
max_leaf_nodesNoneNonemax_featuresautoauto
max_samplesNoneNonemax_leaf_nodesNoneNone
min_impurity_decrease0.000.00max_samplesNoneNone
min_impurity_splitNoneNonemin_impurity_decrease0.000.00
min_samples_leaf11min_samples_leaf11
min_samples_split22min_samples_split22
min_weight_fraction_leaf0.000.00min_weight_fraction_leaf0.000.00
n_estimators100100n_estimators100100
n_jobsNoneNonen_jobsNoneNone
oob_scoreFalseFalseoob_scoreFalseFalse
random_stateNoneNonerandom_stateNoneNone
verbose00verbose00
warm_startFalseFalsewarm_startFalseFalse
\n" @@ -3885,23 +4066,23 @@ "data": { "text/html": [ "\n", - "\n", + "
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Accuracy1.00Accuracy1.00
Precision - Macro Average1.00Precision - Macro Average1.00
Recall - Macro Average1.00Recall - Macro Average1.00
\n" @@ -3963,23 +4144,23 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -3992,16 +4173,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 
Accuracy1.00Accuracy1.00
Precision - Macro Average1.00Precision - Macro Average1.00
Recall - Macro Average1.00Recall - Macro Average1.00
\n" @@ -4039,7 +4220,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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SJC1Eo4Vxkq2B44H9geXAIUmWzxrtcOCGqnoU8F7gL8aqR5KkhWrMI+N9gLVVdWVV3QqcBBw4a5wDgY+37k8Dz0+SEWuSJGnBGTOMdwGunui/prXNOU5V3Q7cCDxkxJokSVpwtuldwDSSrARWtt6bklzRsx5NbWfg+t5F3BfFCzq6i9vZSEbYzh6xvgFjhvG1wG4T/bu2trnGuSbJNsCDgB/PnlBVnQCcMFKdGkmS1VW1oncd0n2Z29l9w5inqc8HliXZI8m2wMHAqlnjrAJe3bpfDny1qmrEmiRJWnBGOzKuqtuTHAmcDmwNfKyqLk1yLLC6qlYBfwN8Isla4CcMgS1J0qISD0Q1liQr2yUGSSNxO7tvMIwlSerMx2FKktSZYbwFJXlrkkuTrElyUZKntPaPzvF0ss2dx03zMZ3NnPdZSTZ4V+c042xJSd5yD1//W/P12ekuSe5o28glSb6QZKd5mu5hSd4/T9O6Ksl3Wp0XJXn6fEx3jvnsneTFs9r2T7I6yWVJLkzyv1v7O5L80TzO+5sT3ce1v1/HJTkiyavmaz73RJKlSS65p+P0dq/4P+P7giRPA34TeFJV3ZJkZ2BbgKr6L12LW9zeAvzZPXj9bwFfBC6bl2o045dVtTdAko8Drwfe1bWiuT23qjbpf3yTbNMecjStvYEVwGnt9XsB7wdeUlXfbY8eXrn+l2++qprcwVgJPLiq7tjU6WzGMi86HhlvOQ8Hrq+qWwCq6vqq+iHc/WgxyU0Te6D/lGSfNvzKJAe0cQ5L8vet/ftJ3j7XDJO8Ocn57Uj8nesZZ5r5bZfk/7ajgAuTPLe1b5/kpCSXJ/kcsP3EdF+U5Jwk305yapIdNveNS/KqtgwXJ/lEa1ua5Kut/Ywku7f2E5O8L8k32zK8vLU/PMnXJ462npXk3cD2re3v2nifT3JBez9WTtRwU5J3tRrOTfKwdiR0AHBcm8YjN3cZtUHn0J7e19bPc9p6+M0kj27thyX5bJIvt23iPTMvTvKaJN9L8i3gGRPtG1qHPtg+5yuT7JvkY209P3FDhW5kmh9Kch7wniSPbLVekOQbSR7TxntFWz8vbuvrtsCxwEFtHTsI+B/Au6rquwBVdUdVfXCOWl7btv+Lk3wmyQPmmkdre1ySb7V5rEmyrLXf1H6vAnYALkhyUCaOwDewLHdb5lm1Hda2ta9kOMNwZJI3ts/13CQPbuPt3frXJPlckv/Q2p/c6r+YYUdtZrpbZ/h7NvN373Ub+rwWlKryZwv8MKzIFwHfAz4APGdi2FnAitZdwP6t+3PAPwL3A54AXNTaDwOuY3h06PbAJROvv6n9fhHDg1LCsNP1ReDZc9Q1zfzexPCvaQCPAf4V2A5440T744HbGfbgdwa+DjywDftj4JjZyzrl+/a49p7t3Pof3H5/AXh16/594POt+0Tg1LbMyxmejz6zDG9t3VsDO06+XxPzm5n+zPv6kIn36aWt+z3A2ybm9/Le69d97WdiPd66fZ77tf5fA7Zp3S8APjOxTVzJ8OCg7YAfMDxQ6OFtfV3CcCbqbOD9U6xDJ7Vt50DgZ8BvtHXqAmDvNt5VwHcYtuvzppjmF4GtW/8ZwLLW/RSGZyzQprdL695pYtneP/HefBt4wnret3cAf9S6HzLR/qfAURuYx18Dh7bubYHtZ28fs7on57O+ZbnbMs+q8zBgLbBj+2xuBI5ow94LvKF1r6H9rWTYKfmrifZnt+7jgEta90ru2jbvD6wG9gCWzoyzUH88Tb2FVNVNSZ4MPAt4LnBykqOr6sRZo94KfLl1fwe4papuS/IdhhVqxleq6scAST4LPJNhxZvxovZzYevfAVjGEJKbOr9nMmys1HBa7AfAnsCzgfe19jVJ1rTxn8oQhGdn+N6PbRmObjbH84BTq50KrKqftPanAb/Tuj/B3fe8P19VdwKXJXlYazsf+FiS+7XhF61nfn+Q5Ldb924M79mPGd6nL7b2C4AXbubyaDrbJ7mI4Yj4cuArrf1BwMfbkVsx7DjOOKOqbgRIchnDowd3Bs6qqnWt/WSGdRc2vA59oaqqbQf/r6q+015/KcN2cVEbb/Zp6g1N89SquiPDWaKnA6fmru/FuX/7fTZwYpJTgM9u6A2awl5J/hTYiWH7P30D8zgHeGuSXYHPVtX3p5nBRpYF2jKv5+VnVtXPgZ8nuZFhRwaGv0OPT/Ighp2Fr7X2j7f57NTaZ/6WfYLh2wFh+Jv3+LQzYgzryzKGHfoFzdPUW1ANp5POqqq3A0cCL5tjtNuq7dYBdwIzp7Xv5O7X+Gf/T9rs/gB/XlV7t59HVdXf3IP5bYow7CzMzHt5VR2+3pGTp+Sum2AO2Mx5TrplVi20DffZDI9gPTFz3HySZF+Go62nVdUTGHZktmuDJ9+nO/B+i7HNXDN+BMNnOHMq8k8Y/ojvBbyUuz4fuPvnfk8/o5lp3cndp3tPtotftN9bAT+d2D72rqrHAlTVEcDbGHYEL0gy1xfnXAo8eYr5nQgcWVW/AbyT9l7NNY+q+iTDJZdfAqcled6Uy7TeZZm1zHOZ/b5Ovuf35G/PURO17FFV/7iZ09qiDOMtJMmjZ67DNHsznErbXC9M8uAk2zPcRHT2rOGnA7/f9lxJskuSh27mvL4BHNqmsyewO3AFw1H2f27tezGcqgY4F3hGkke1YQ9sr5tTVZ03sfHMfmTqV4FXzPxRmrmWBHyTu57Ydmircb2SPILhCOcjwEeBJ7VBt7WjZRj2om+oqpvbda+nbmiazc8ZTrVpBFV1M/AHwJty1/PrZ55xf9gUkzgPeE6Sh7TP+RUTwzZpHZrSRqdZVT8D/iXJKwAyeELrfmTbHo4B1jEE5ux17DjgLTPbVJKtkhwxRy07Ate15T50pnGueST5deDKqnof8PfctS1v0IaW5Z5qZzluSPKs1vR7wNeq6qfAT5M8s7UfOvGy04H/OrNNJ9kzyQPno56xGcZbzg4Mp9cua6dzlzNcd9lc3wI+w3Dt5DNVNXmKmrY3+EngnHaq7dNsfmh8ANiqTedk4LAabkT7ILBDkssZrudc0Oa9juEP5afasp7DcK15k1XVpQx30X6t3azxl23QUcBr2vR/D/jDjUxqX+DiJBcCBwH/p7WfAKzJcAPXl4Ft2vK8m2GnYmNOAt7cbjzxBq4RVNWFDOv5IQynff+8fY4bPXqqqusYtrNzGHZYL58YvKnr0DSmneahwOFtnb6Uu77r/bgMN0pewhDsFwNnAsvbmaODqmoN8AaG7etyhnsbfn2Oefwvhp2Rs4HvTrTPNY/fBS5plwb2Av52E5Z5fcsyH17d6l3DcABzbGt/DXB8qzcT43+U4T8bvt2W78PcS85i+QSue6EkhzHcBHVk71okSfecR8aSJHXmkbEkSZ15ZCxJUmeGsSRJnRnGkiR1ZhhLktSZYSxJUmeGsSRJnf1/SvXV4oV77AUAAAAASUVORK5CYII=\n", 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" ] @@ -4197,7 +4378,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.8.6" }, "toc": { "base_numbering": 1, From 444e9ba97fca239215dc8c76ff0a3c799ae269f1 Mon Sep 17 00:00:00 2001 From: Matan Perlmutter Date: Tue, 7 Dec 2021 15:28:15 +0200 Subject: [PATCH 4/7] Fix pylint --- deepchecks/base/check.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deepchecks/base/check.py b/deepchecks/base/check.py index 77ab4973a8..7922dd1911 100644 --- a/deepchecks/base/check.py +++ b/deepchecks/base/check.py @@ -181,6 +181,7 @@ def __repr__(self): return self.value.__repr__() def get_header(self): + """Return header for display. if header was defined return it, else extract name of check class.""" if self.header is not None: return self.header else: From 069106a56ac7a9b7e76ec24234267aa460ec3398 Mon Sep 17 00:00:00 2001 From: Matan Perlmutter Date: Tue, 7 Dec 2021 15:28:46 +0200 Subject: [PATCH 5/7] Fix pylint --- deepchecks/base/check.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/deepchecks/base/check.py b/deepchecks/base/check.py index 7922dd1911..e8531ba5dc 100644 --- a/deepchecks/base/check.py +++ b/deepchecks/base/check.py @@ -182,10 +182,7 @@ def __repr__(self): def get_header(self): """Return header for display. if header was defined return it, else extract name of check class.""" - if self.header is not None: - return self.header - else: - return self.check.name() + return self.header or self.check.name() def set_condition_results(self, results: List[ConditionResult]): """Set the conditions results for current check result.""" From 6c645c894d2d50fd02d11d392bd8b2f0e1017dee Mon Sep 17 00:00:00 2001 From: Matan Perlmutter Date: Tue, 7 Dec 2021 15:31:00 +0200 Subject: [PATCH 6/7] Fix notebook --- .../datasets_size_comparison.ipynb | 36 +++++++++++-------- 1 file changed, 22 insertions(+), 14 deletions(-) diff --git a/notebooks/checks/methodology/datasets_size_comparison.ipynb b/notebooks/checks/methodology/datasets_size_comparison.ipynb index 58ac3b3337..715195c54b 100644 --- a/notebooks/checks/methodology/datasets_size_comparison.ipynb +++ b/notebooks/checks/methodology/datasets_size_comparison.ipynb @@ -52,7 +52,16 @@ { "data": { "text/html": [ - "

Datasets size.

" + "

Datasets Size Comparison

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Verify Test dataset size comparing it to the Train dataset size.

" ] }, "metadata": {}, @@ -62,23 +71,23 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", @@ -88,9 +97,9 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 
size600400size600400
\n" @@ -127,7 +136,7 @@ "hash": "84907e5c832b0f30997d577d5af975f753923a883501768793aa42b5515ede32" }, "kernelspec": { - "display_name": "Python 3.8.10 64-bit ('.deepchecks-venv': venv)", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -141,9 +150,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "orig_nbformat": 4 + "version": "3.8.6" + } }, "nbformat": 4, "nbformat_minor": 2 From 34c6c631d21e64b2227128560eb8ca25d6f967a3 Mon Sep 17 00:00:00 2001 From: Matan Perlmutter Date: Tue, 7 Dec 2021 15:47:27 +0200 Subject: [PATCH 7/7] Recover deleted files --- deepchecks/base/check.py | 5 +- examples/drafts/checks_demonstration.ipynb | 1495 ++++++++++++++++++++ examples/drafts/condition_demo.ipynb | 769 ++++++++++ examples/drafts/date_leakage.ipynb | 455 ++++++ examples/drafts/suite_demonstration.ipynb | 340 +++++ 5 files changed, 3062 insertions(+), 2 deletions(-) create mode 100644 examples/drafts/checks_demonstration.ipynb create mode 100644 examples/drafts/condition_demo.ipynb create mode 100644 examples/drafts/date_leakage.ipynb create mode 100644 examples/drafts/suite_demonstration.ipynb diff --git a/deepchecks/base/check.py b/deepchecks/base/check.py index e8531ba5dc..ec1f0fd631 100644 --- a/deepchecks/base/check.py +++ b/deepchecks/base/check.py @@ -205,12 +205,12 @@ def get_conditions_sort_value(self): return max([r.get_sort_value() for r in self.conditions_results]) -def wrap_run(func, clazz_instance): +def wrap_run(func, class_instance): """Wrap the run function of checks, and sets the `check` property on the check result.""" @wraps(func) def wrapped(*args, **kwargs): result = func(*args, **kwargs) - result.check = clazz_instance.__class__ + result.check = class_instance.__class__ return result return wrapped @@ -224,6 +224,7 @@ class BaseCheck(metaclass=abc.ABCMeta): def __init__(self): self._conditions = OrderedDict() self._conditions_index = 0 + # Replace the run function with wrapped run function setattr(self, 'run', wrap_run(getattr(self, 'run'), self)) def conditions_decision(self, result: CheckResult) -> List[ConditionResult]: diff --git a/examples/drafts/checks_demonstration.ipynb b/examples/drafts/checks_demonstration.ipynb new file mode 100644 index 0000000000..e85030ab73 --- /dev/null +++ b/examples/drafts/checks_demonstration.ipynb @@ -0,0 +1,1495 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f7d1e20", + "metadata": {}, + "source": [ + "## Imports and configurations" + ] + }, + { + "cell_type": "markdown", + "id": "9254315d", + "metadata": {}, + "source": [ + "### Config directory path for demo datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "da391550", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:36.150652Z", + "start_time": "2021-11-02T13:17:36.141249Z" + } + }, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "80c06c83", + "metadata": {}, + "source": [ + "NOTE - Set DATASETS_BASEDIR to your local folder that contains all required datasets.
\n", + "Datasets can be found in shared folder:
https://drive.google.com/drive/u/0/folders/1WIjlwoUdgwrQj1S9UmJLMbJT6NuKeX7t" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5dca81c7", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:36.162982Z", + "start_time": "2021-11-02T13:17:36.158671Z" + } + }, + "outputs": [], + "source": [ + "DATASETS_BASEDIR = '../../../../Datasets'" + ] + }, + { + "cell_type": "markdown", + "id": "1448791a", + "metadata": {}, + "source": [ + "Load dataset paths" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "85ba0400", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:36.176979Z", + "start_time": "2021-11-02T13:17:36.170787Z" + } + }, + "outputs": [], + "source": [ + "# verify that DATASETS_BASEDIR exists a\n", + "dataset_names = os.listdir(DATASETS_BASEDIR)\n", + "# print(dataset_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "47e0e1d3", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:36.189265Z", + "start_time": "2021-11-02T13:17:36.184003Z" + } + }, + "outputs": [], + "source": [ + "# List all datasets used\n", + "DATASET_PATHS = {}\n", + "DATASET_PATHS['Lending_Club'] = os.path.join(DATASETS_BASEDIR, 'Lending Club')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "33c8513c", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:36.202774Z", + "start_time": "2021-11-02T13:17:36.196342Z" + } + }, + "outputs": [], + "source": [ + "for dataset_name in DATASET_PATHS:\n", + " if not os.path.exists(DATASET_PATHS[dataset_name]):\n", + " print(\"Verify that all required datasets are in your datasets folder!\")\n", + " raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), DATASET_PATHS[dataset_name])" + ] + }, + { + "cell_type": "markdown", + "id": "298e795f", + "metadata": {}, + "source": [ + "### General Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c0e6b0d5", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:49.764501Z", + "start_time": "2021-11-02T13:17:41.830342Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import joblib\n", + "import errno" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2ab583b4", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:56.182643Z", + "start_time": "2021-11-02T13:17:49.767136Z" + } + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier" + ] + }, + { + "cell_type": "markdown", + "id": "205ba279", + "metadata": {}, + "source": [ + "### Imports for checks" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2a0dd36e", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:18:00.007037Z", + "start_time": "2021-11-02T13:17:56.184529Z" + } + }, + "outputs": [], + "source": [ + "import mlchecks\n", + "from mlchecks.base import Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "54ee33da", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:35:39.678398Z", + "start_time": "2021-11-02T13:35:39.673180Z" + } + }, + "outputs": [], + "source": [ + "# Note - all checks are initialized also in mlchecks.checks and can be imported directly from there\n", + "# Demonstration here it is just for the sake of order" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ab40619e", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:34:54.777509Z", + "start_time": "2021-11-02T13:34:54.772540Z" + }, + "code_folding": [] + }, + "outputs": [], + "source": [ + "# Overview\n", + "from mlchecks.checks.overview import dataset_info, DatasetInfo\n", + "from mlchecks.checks.overview import model_info, ModelInfo\n", + "from mlchecks.checks.overview import feature_importance, FeatureImportance" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0e2833c5", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T18:52:36.361482Z", + "start_time": "2021-11-02T18:52:36.355208Z" + } + }, + "outputs": [], + "source": [ + "# Integrity\n", + "\n", + "from mlchecks.checks.integrity import data_duplicates, DataDuplicates\n", + "from mlchecks.checks.integrity import dominant_frequency_change, DominantFrequencyChange\n", + "from mlchecks.checks.integrity import is_single_value, IsSingleValue\n", + "from mlchecks.checks.integrity import mixed_nulls, MixedNulls\n", + "from mlchecks.checks.integrity import mixed_types, MixedTypes\n", + "from mlchecks.checks.integrity import new_category_train_validation, CategoryMismatchTrainTest\n", + "from mlchecks.checks.integrity import new_label_train_validation, NewLabelTrainTest\n", + "from mlchecks.checks.integrity import rare_format_detection, RareFormatDetection\n", + "from mlchecks.checks.integrity import special_characters, SpecialCharacters\n", + "from mlchecks.checks.integrity import string_length_outlier, StringLengthOutlier\n", + "from mlchecks.checks.integrity import string_mismatch, StringMismatch\n", + "from mlchecks.checks.integrity import string_mismatch_comparison, StringMismatchComparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8836ac5", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:34:19.900597Z", + "start_time": "2021-11-02T13:34:19.894115Z" + } + }, + "outputs": [], + "source": [ + "# Leakage\n", + "\n", + "from mlchecks.checks.leakage import data_sample_leakage_report, DataSampleLeakageReport\n", + "\n", + "from mlchecks.checks.leakage import date_train_validation_leakage_overlap, DateTrainTestLeakageOverlap\n", + "from mlchecks.checks.leakage import date_train_validation_leakage_duplicates, DateTrainTestLeakageDuplicates\n", + "\n", + "from mlchecks.checks.leage import single_feature_contribution, SingleFeatureContribution\n", + "from mlchecks.checks.leage import single_feature_contribution_train_validation, SingleFeatureContributionTrainTest\n", + "\n", + "from mlchecks.checks.leakage import index_train_validation_leakage, IndexTrainTestLeakage" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2161be95", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:34:19.900597Z", + "start_time": "2021-11-02T13:34:19.894115Z" + } + }, + "outputs": [], + "source": [ + "from mlchecks.checks.performance import performance_report, confusion_matrix_report, PerformanceReport, ConfusionMatrixReport" + ] + }, + { + "cell_type": "markdown", + "id": "17e6b95d", + "metadata": {}, + "source": [ + "#### Leakage" + ] + }, + { + "cell_type": "markdown", + "id": "b75e4443", + "metadata": {}, + "source": [ + "#### " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "470f7e49", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:31:59.032058Z", + "start_time": "2021-11-02T13:31:46.683569Z" + } + }, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'BoostingOverfit' from 'mlchecks.checks.overfit' (/mnt/c/Users/Shir/NoSync_Documents/Git/MLChecks/mlchecks/checks/overfit/__init__.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_8883/810330316.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Checks that were in demo but aren't in master yet:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrare_format_detection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRareFormatDetection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrare_format_detection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverfit\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mboosting_overfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBoostingOverfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverfit\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mperformance_overfit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerformanceOverfit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmlchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintegrity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset_drift\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdataset_drift\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'BoostingOverfit' from 'mlchecks.checks.overfit' (/mnt/c/Users/Shir/NoSync_Documents/Git/MLChecks/mlchecks/checks/overfit/__init__.py)" + ] + } + ], + "source": [ + "# additiona\n", + "from mlchecks.checks.integrity.rare_format_detection import RareFormatDetection, rare_format_detection\n", + "from mlchecks.checks.overfit import boosting_overfit, BoostingOverfit\n", + "from mlchecks.checks.overfit import performance_overfit, PerformanceOverfit\n", + "from mlchecks.checks.integrity.dataset_drift import dataset_drift" + ] + }, + { + "cell_type": "markdown", + "id": "facdec56", + "metadata": {}, + "source": [ + "## Lending Club" + ] + }, + { + "cell_type": "markdown", + "id": "3522957a", + "metadata": {}, + "source": [ + "### Load Data & Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08ecb914", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:16:25.582726Z", + "start_time": "2021-11-02T13:16:22.526799Z" + } + }, + "outputs": [], + "source": [ + "lending_club_path = DATASET_PATHS['Lending_Club']\n", + "df_train_lending_club = pd.read_csv(os.path.join(lending_club_path, 'train.csv'))\n", + "df_train_lending_club.issue_d = pd.to_datetime(df_train.issue_d)\n", + "df_val_lending_club = pd.read_csv(os.path.join(lending_club_path, 'test.csv'))\n", + "df_val.issue_d = pd.to_datetime(df_val.issue_d)\n", + "lending_club_catboost_clf = joblib.load(os.path.join(lending_club_path, 'model.joblib'))" + ] + }, + { + "cell_type": "markdown", + "id": "6650378f", + "metadata": {}, + "source": [ + "#### Define Metadata for Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c884cbd4", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:52.273738Z", + "start_time": "2021-11-01T12:29:52.268488Z" + } + }, + "outputs": [], + "source": [ + "# dataset metadata (manaul)\n", + "\n", + "categorical_features = ['addr_state',\n", + " 'application_type',\n", + "# 'disbursement_method',\n", + "# 'grade',\n", + " 'home_ownership',\n", + " 'initial_list_status',\n", + " 'purpose',\n", + " 'term',\n", + " 'verification_status']\n", + "\n", + "all_features = ['sub_grade', 'term', 'home_ownership', 'fico_range_low',\n", + " 'total_acc', 'pub_rec', 'revol_util', 'annual_inc', 'int_rate', 'dti',\n", + " 'purpose', 'mort_acc', 'loan_amnt', 'application_type', 'installment',\n", + " 'verification_status', 'pub_rec_bankruptcies', 'addr_state',\n", + " 'initial_list_status', 'fico_range_high', 'revol_bal', 'open_acc',\n", + " 'emp_length', 'time_to_earliest_cr_line']\n", + "\n", + "label_col_name = 'loan_status'\n", + "index_col_name = 'id'\n", + "date_col_name = 'issue_d'\n", + "# label_name_dict = {0: \"Default\", 1: \"OK\"}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6d3c070", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:52.721742Z", + "start_time": "2021-11-01T12:29:52.712161Z" + } + }, + "outputs": [], + "source": [ + "df_train_lending_club.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fee781b", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:17:08.205962Z", + "start_time": "2021-11-02T13:17:05.888123Z" + } + }, + "outputs": [], + "source": [ + "ds_train_lending_club = Dataset(df_train_lending_club, cat_features = categorical_features, features=all_features,\n", + " label = label_col_name, index = index_col_name, date=date_col_name)\n", + "ds_val_lending_club = Dataset(df_val_lending_club, cat_features = categorical_features, features=all_features,\n", + " label = label_col_name, index = index_col_name, date=date_col_name)" + ] + }, + { + "cell_type": "markdown", + "id": "869023f2", + "metadata": {}, + "source": [ + "### Additional for showing validation faults\n" + ] + }, + { + "cell_type": "markdown", + "id": "1edcd16a", + "metadata": {}, + "source": [ + "#### demo util function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dcf3d23", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:55.831236Z", + "start_time": "2021-11-01T12:29:55.826981Z" + } + }, + "outputs": [], + "source": [ + "def dataset_from_dict(d: dict, index_name: str = None) -> Dataset:\n", + " dataframe = pd.DataFrame(data=d)\n", + " return Dataset(dataframe, index=index_name)" + ] + }, + { + "cell_type": "markdown", + "id": "f3fe6164", + "metadata": {}, + "source": [ + "#### demo data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "53e23423", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:56.805419Z", + "start_time": "2021-11-01T12:29:56.799279Z" + } + }, + "outputs": [], + "source": [ + "# mixed nulls\n", + "mixed_nulls_demo_data = {'col1': ['nan', None, 'null', 'Nan', '1', 'cat'], 'col2':['', '', 'None', 'a', 'b', 'c'], 'col3': [1,2,3,4,5,6]}\n", + "df_mixed_nulls = pd.DataFrame(data=mixed_nulls_demo_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "00ee0294", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:57.278581Z", + "start_time": "2021-11-01T12:29:57.272334Z" + } + }, + "outputs": [], + "source": [ + "# single value\n", + "df_single_value_demo = pd.DataFrame({'a':[3,4,1], 'b':[2,2,2], 'c':[None, None, None], 'd':['a', 4, 6]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "010bd416", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:57.635045Z", + "start_time": "2021-11-01T12:29:57.624211Z" + } + }, + "outputs": [], + "source": [ + "# synthetic index leakage\n", + "train_df_synthetic_leakage = dataset_from_dict({'col1': [1, 2, 3, 4, 10, 11]}, 'col1')\n", + "val_df_synthetic_leakage = dataset_from_dict({'col1': [4, 3, 5, 6, 7]}, 'col1')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5181e28", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:57.965809Z", + "start_time": "2021-11-01T12:29:57.958939Z" + } + }, + "outputs": [], + "source": [ + "# string mismatch data\n", + "data = {'col1': ['Deep', 'deep', 'deep!!!', '$deeP$', 'earth', 'foo', 'bar', 'foo?']}\n", + "df_string_mismatch = pd.DataFrame(data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8a0a254", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:58.396481Z", + "start_time": "2021-11-01T12:29:58.377096Z" + } + }, + "outputs": [], + "source": [ + "# index leakage\n", + "iris = load_iris(as_frame=True)\n", + "frame = iris.frame\n", + "X = iris.data\n", + "y = iris.target\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=55)\n", + "train_ds_index_leakage = Dataset(pd.concat([X_train, y_train], axis=1), \n", + " features=iris.feature_names,\n", + " label='target')\n", + "\n", + "test_df = pd.concat([X_test, y_test], axis=1)\n", + "bad_test = test_df.append(train_ds_index_leakage.data.iloc[[0, 1, 2, 3, 4]], ignore_index=True)\n", + " \n", + "val_ds_index_leakage = Dataset(bad_test, \n", + " features=iris.feature_names,\n", + " label='target')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc69a19a", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:29:58.987167Z", + "start_time": "2021-11-01T12:29:58.966313Z" + } + }, + "outputs": [], + "source": [ + "# rare format detection\n", + "df = pd.DataFrame(np.random.choice(a=['BIG', 'STILL_BIG'], size=(200,3)), columns=['x1', 'x2', 'x3'])\n", + "df = df.append({'x1': 'bla', 'x2': 'BIG', 'x3': 1}, ignore_index=True)\n", + "df = df.append({'x1': 'bla', 'x2': 'BIG', 'x3': 1}, ignore_index=True)\n", + "rare_format_df = df.append({'x1': 'bla2', 'x2': 'BIG', 'x3': 2}, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71254dc0", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:03:03.809278Z", + "start_time": "2021-11-02T13:03:03.646330Z" + } + }, + "outputs": [], + "source": [ + "# multiclass models - adaboost, randomforest (e.g. for overfit check)\n", + "iris = load_iris(as_frame=True)\n", + "frame = iris.frame\n", + "X_iris = iris.data\n", + "Y_iris = iris.target\n", + "X_train_iris, X_test_iris, y_train_iris, y_test_iris = train_test_split(\n", + " X, Y, test_size=0.33, random_state=42)\n", + "ds_train_iris = Dataset(pd.concat([X_train_iris, y_train_iris], axis=1), \n", + " features=iris.feature_names,\n", + " label='target')\n", + "ds_val_iris = Dataset(pd.concat([X_test_iris, y_test_iris], axis=1), \n", + " features=iris.feature_names,\n", + " label='target')\n", + "iris_multiclass_adaboost_clf = AdaBoostClassifier()\n", + "iris_multiclass_adaboost_clf.fit(ds_train_iris.data.drop(ds_train_iris.label_name(), axis=1), ds_train_iris.label_col())\n", + "iris_multiclass_rf_clf = RandomForestClassifier()\n", + "iris_multiclass_rf_clf.fit(ds_train_iris.data.drop(ds_train_iris.label_name(), axis=1), ds_train_iris.label_col())" + ] + }, + { + "cell_type": "markdown", + "id": "73462329", + "metadata": {}, + "source": [ + "##### Drift demo data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed46afa8", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:00.314390Z", + "start_time": "2021-11-01T12:30:00.307216Z" + } + }, + "outputs": [], + "source": [ + "# Commented out all this cause drift feature isn't in master yet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81bb9cd3", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:00.570780Z", + "start_time": "2021-11-01T12:30:00.565844Z" + } + }, + "outputs": [], + "source": [ + "# df = pd.read_csv(os.path.join(KKBOX_DATASET_BASEDIR, 'train_clean.csv'))\n", + "# test_df = pd.read_csv(os.path.join(KKBOX_DATASET_BASEDIR, 'test_clean.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e43a4f39", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:00.726259Z", + "start_time": "2021-11-01T12:30:00.722716Z" + } + }, + "outputs": [], + "source": [ + "# test_df.date = pd.to_datetime(test_df.date*10**9)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d81fe7b7", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:00.916335Z", + "start_time": "2021-11-01T12:30:00.910552Z" + } + }, + "outputs": [], + "source": [ + "# drift_org_dataset = Dataset(test_df, \n", + "# features=['num_25', 'num_50', 'num_75', 'num_985', 'num_100', 'num_unq',\n", + "# 'total_secs', 'days_listened', 'plan_list_price', 'is_auto_renew',\n", + "# 'is_cancel', 'gender', 'registered_via', 'secs_per_song', 'num_days'],\n", + "# label='y_true49a0c676-35fd-11ea-978f-2e728ce88125',\n", + "# cat_features= ['gender', 'registered_via'],\n", + "# index='msno', date='date')\n", + "\n", + "# drift_compared_dataset = Dataset(df,\n", + "# features=['num_25', 'num_50', 'num_75', 'num_985', 'num_100', 'num_unq',\n", + "# 'total_secs', 'days_listened', 'plan_list_price', 'is_auto_renew',\n", + "# 'is_cancel', 'gender', 'registered_via', 'secs_per_song', 'num_days'],\n", + "# label='y_true49a0c676-35fd-11ea-978f-2e728ce88125',\n", + "# cat_features= ['gender', 'registered_via'],\n", + "# index='msno')" + ] + }, + { + "cell_type": "markdown", + "id": "316aceb1", + "metadata": {}, + "source": [ + "## Run checks" + ] + }, + { + "cell_type": "markdown", + "id": "83245c45", + "metadata": {}, + "source": [ + "### Overview" + ] + }, + { + "cell_type": "markdown", + "id": "6f254965", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### Dataset Info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2dbc02c", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.312426Z", + "start_time": "2021-11-01T12:30:01.515239Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "dataset_info(ds_train)" + ] + }, + { + "cell_type": "markdown", + "id": "5145a944", + "metadata": {}, + "source": [ + "#### Feature Importance (SHAP)" + ] + }, + { + "cell_type": "markdown", + "id": "8485039b", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "##### Binary Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b861b71a", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:14:55.938309Z", + "start_time": "2021-11-02T13:14:55.928234Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "ds_train.data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc9a1901", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:14:31.929720Z", + "start_time": "2021-11-02T13:13:25.516894Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "feature_importance(ds_train, lending_club_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd4e5cda", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:13:19.347212Z", + "start_time": "2021-11-02T13:13:19.347182Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "res" + ] + }, + { + "cell_type": "markdown", + "id": "4114c894", + "metadata": {}, + "source": [ + "##### Multi-class Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6d048f5", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:13:22.280987Z", + "start_time": "2021-11-02T13:13:21.432169Z" + } + }, + "outputs": [], + "source": [ + "feature_importance(ds_train_iris, iris_multiclass_rf_clf)" + ] + }, + { + "cell_type": "markdown", + "id": "29f97a59", + "metadata": {}, + "source": [ + "#### Model Info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a65f5ff7", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:03:22.273598Z", + "start_time": "2021-11-02T13:03:22.230310Z" + } + }, + "outputs": [], + "source": [ + "model_info(lending_club_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03520051", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-02T13:03:41.985923Z", + "start_time": "2021-11-02T13:03:41.971434Z" + } + }, + "outputs": [], + "source": [ + "model_info(iris_multiclass_adaboost_clf)" + ] + }, + { + "cell_type": "markdown", + "id": "cc05c824", + "metadata": {}, + "source": [ + "### Integrity" + ] + }, + { + "cell_type": "markdown", + "id": "53edb405", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### Mixed Nulls" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87f0e080", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.471141Z", + "start_time": "2021-11-01T12:30:20.455225Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "mixed_nulls(df_mixed_nulls)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6cf1f72", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.591320Z", + "start_time": "2021-11-01T12:30:20.474061Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "mixed_nulls(df_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66598aa4", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.650286Z", + "start_time": "2021-11-01T12:30:20.592816Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "mixed_nulls(df_val)" + ] + }, + { + "cell_type": "markdown", + "id": "1f4debad", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### Single Value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1fe64c4b", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.663837Z", + "start_time": "2021-11-01T12:30:20.651838Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "is_single_value(df_single_value_demo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "037539e1", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.797905Z", + "start_time": "2021-11-01T12:30:20.665716Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "is_single_value(df_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2abf8d44", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.866441Z", + "start_time": "2021-11-01T12:30:20.799304Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "is_single_value(df_val)" + ] + }, + { + "cell_type": "markdown", + "id": "8c8eec86", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### String Mismatch - till here done but not updated" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16ca6eac", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.889247Z", + "start_time": "2021-11-01T12:30:20.868289Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "df_string_mismatch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "516353c6", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.904494Z", + "start_time": "2021-11-01T12:30:20.892884Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "string_mismatch(df_string_mismatch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9eec6bd", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:20.993699Z", + "start_time": "2021-11-01T12:30:20.905944Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "string_mismatch(df_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b23bb5d", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.040501Z", + "start_time": "2021-11-01T12:30:20.995400Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "string_mismatch(df_val)" + ] + }, + { + "cell_type": "markdown", + "id": "3c864b23", + "metadata": {}, + "source": [ + "#### From here TODO" + ] + }, + { + "cell_type": "markdown", + "id": "20ceaaf7", + "metadata": {}, + "source": [ + "#### Data Duplicates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe5e08e1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "919eb60e", + "metadata": {}, + "source": [ + "#### Dominant Frequency Change" + ] + }, + { + "cell_type": "markdown", + "id": "e696a7fc", + "metadata": {}, + "source": [ + "#### Mixed Types" + ] + }, + { + "cell_type": "markdown", + "id": "9e5c4f05", + "metadata": {}, + "source": [ + "#### New Category" + ] + }, + { + "cell_type": "markdown", + "id": "e8110579", + "metadata": {}, + "source": [ + "#### New Label" + ] + }, + { + "cell_type": "markdown", + "id": "deaa9c27", + "metadata": {}, + "source": [ + "#### Special Characters" + ] + }, + { + "cell_type": "markdown", + "id": "e7745bab", + "metadata": {}, + "source": [ + "#### String Length Outlier" + ] + }, + { + "cell_type": "markdown", + "id": "51b88d26", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:49:36.033828Z", + "start_time": "2021-11-01T12:49:36.029516Z" + } + }, + "source": [ + "#### String Mismatch Comparison" + ] + }, + { + "cell_type": "markdown", + "id": "cd1e0989", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### Rare Format Detection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c5bd5ea", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.044341Z", + "start_time": "2021-11-01T12:30:21.041751Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "# rare_format_detection(rare_format_df)\n", + "# rare_format_df = df.append({'x1': 'bla2', 'x2': 'BIG', 'x3': 2}, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d96d457", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.050396Z", + "start_time": "2021-11-01T12:30:21.045697Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "# rare_format_detection(df_train)" + ] + }, + { + "cell_type": "markdown", + "id": "e9c5c651", + "metadata": {}, + "source": [ + "### Overfit" + ] + }, + { + "cell_type": "markdown", + "id": "0b7dd6c8", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.062636Z", + "start_time": "2021-11-01T12:30:21.057850Z" + } + }, + "source": [ + "#### TODO - Boosting Overfit" + ] + }, + { + "cell_type": "markdown", + "id": "eb56a773", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.067245Z", + "start_time": "2021-11-01T12:30:21.064362Z" + } + }, + "source": [ + "#### TODO - Performance Overfit" + ] + }, + { + "cell_type": "markdown", + "id": "8e36f433", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "### Drift" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1845afe", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:56:39.791532Z", + "start_time": "2021-11-01T12:56:39.788459Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "# TBD" + ] + }, + { + "cell_type": "markdown", + "id": "31251702", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "### Leakage" + ] + }, + { + "cell_type": "markdown", + "id": "7a63405e", + "metadata": { + "hidden": true + }, + "source": [ + "#### Index Train-Validation Leakage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "229a328f", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.083355Z", + "start_time": "2021-11-01T12:30:21.073620Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "index_train_validation_leakage(train_df_synthetic_leakage, val_df_synthetic_leakage)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1514648", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.137582Z", + "start_time": "2021-11-01T12:30:21.084670Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "index_train_validation_leakage(ds_train, ds_val)" + ] + }, + { + "cell_type": "markdown", + "id": "bd2dbb01", + "metadata": { + "hidden": true + }, + "source": [ + "#### Data Sample Leakage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de0c3876", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.161434Z", + "start_time": "2021-11-01T12:30:21.139387Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "data_sample_leakage_report(val_ds_index_leakage, train_ds_index_leakage)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae66f715", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:30:21.165733Z", + "start_time": "2021-11-01T12:30:21.163443Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "# data_sample_leakage_report(ds_val, ds_train)" + ] + }, + { + "cell_type": "markdown", + "id": "5c20ece0", + "metadata": { + "hidden": true + }, + "source": [ + "#### TODO - Single Feature Contribution" + ] + }, + { + "cell_type": "markdown", + "id": "7f9f8da1", + "metadata": {}, + "source": [ + "### Performance" + ] + }, + { + "cell_type": "markdown", + "id": "26192191", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### Performance Report" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c239deb3", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:32:10.072389Z", + "start_time": "2021-11-01T12:32:10.068836Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "cls_report_check = PerformanceReport()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3342531", + "metadata": { + "ExecuteTime": { + "end_time": "2021-11-01T12:32:13.059242Z", + "start_time": "2021-11-01T12:32:12.501950Z" + }, + "hidden": true + }, + "outputs": [], + "source": [ + "cls_report_check.run(ds_val, lending_club_model)" + ] + }, + { + "cell_type": "markdown", + "id": "78323edf", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### TODO - Confusion Matrix Report" + ] + }, + { + "cell_type": "markdown", + "id": "6b05f207", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### TODO - Naive Comparison" + ] + }, + { + "cell_type": "markdown", + "id": "78a83fab", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "#### TODO - ROC Report" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/drafts/condition_demo.ipynb b/examples/drafts/condition_demo.ipynb new file mode 100644 index 0000000000..6c05ca120d --- /dev/null +++ b/examples/drafts/condition_demo.ipynb @@ -0,0 +1,769 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "aa401fe6", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e54802d4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "My Single Suite: [\n", + "\tIsSingleValue\n", + "\tMixedNulls(check_nan=True)\n", + "\t\tConditions:\n", + "\t\t\t0: Not more than 3 different null types for all columns\n", + "\tStringMismatch\n", + "\t\tConditions:\n", + "\t\t\t0: No string variants for all columns\n", + "\tStringMismatch_2\n", + "\t\tConditions:\n", + "\t\t\t0: Not more than 35.00% variants for all columns\n", + "\tMixedTypes\n", + "\t\tConditions:\n", + "\t\t\t0: Rare type ratio is not less than 40.00% of samples in all columns\n", + "\tMixedTypes_2\n", + "\t\tConditions:\n", + "\t\t\t0: Rare type ratio is not less than 10.00% of samples in all columns\n", + "\tRareFormatDetection(patterns=[, , , , , , , ], rarity_threshold=0.05, min_unique_common_ratio=0.01, pattern_match_method=first)\n", + "\t\tConditions:\n", + "\t\t\t0: fail example\n", + "]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from mlchecks import Suite, ConditionResult, ConditionCategory, Dataset\n", + "from mlchecks.checks import *\n", + "\n", + "def condition_exc(r):\n", + " raise Exception('Failed because I need an example')\n", + "\n", + "data = {\n", + " 'col1': ['', '#@$', 'Nan!', '#nan', ''],\n", + " 'col2': ['gabbay', 'GABBAY!!!', 'is', '...', '?Gabbay?'],\n", + " 'col3': [1, 's', 'a', 'b', 'c'],\n", + " 'col4': ['a', 'a', 'a', 'a', 'a']\n", + "}\n", + "\n", + "dataset = Dataset(pd.DataFrame(data=data))\n", + "suite = Suite('My Single Suite',\n", + " IsSingleValue(),\n", + " MixedNulls().add_condition_different_nulls_not_more_than(3),\n", + " StringMismatch().add_condition_no_variants(),\n", + " StringMismatch().add_condition_ratio_variants_not_more_than(0.35),\n", + " MixedTypes().add_condition_rare_type_ratio_not_less_than(0.4),\n", + " MixedTypes().add_condition_rare_type_ratio_not_less_than(0.1),\n", + " RareFormatDetection().add_condition('fail example', condition_exc)\n", + ")\n", + "suite" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "231d6218", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=7, style=ProgressStyle(bar_color='#9…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

My Single Suite

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Conditions Summary

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StatusCheckConditionMore Info
✖
Mixed Nulls - Validation DatasetNot more than 3 different null types for all columnsFound columns with more than 3 null types: col1
✖
String Mismatch - Validation DatasetNot more than 35.00% variants for all columnsFound columns with variants ratio: {'col1': '100%', 'col2': '60.00%'}
✖
Mixed Types - Validation DatasetRare type ratio is not less than 40.00% of samples in all columnsFound columns with low type ratio: col3
!
String Mismatch - Validation DatasetNo string variants for all columnsFound columns with variants: {'col1': ['', 'nan'], 'col2': ['gabbay']}
✓
Mixed Types - Validation DatasetRare type ratio is not less than 10.00% of samples in all columns
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Other Checks Summary (no conditions defined)

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Check
Single Value in Column - Validation Dataset
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Checks that raised an error during run

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CheckError
RareFormatDetectionFailed because I need an example
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Checks that didn't pass condition

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Mixed Nulls - Validation Dataset

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Search for various types of null values in a string column(s), including string representations of null.

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  CountPercent of data
Column NameValue  
col1120.00%
#@$120.00%
Nan!120.00%
#nan120.00%
120.00%
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String Mismatch - Validation Dataset

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Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

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  ValueCount% In data
Column NameBase form   
col1120.00%
#@$120.00%
nan#nan120.00%
nan120.00%
nanNan!120.00%
col2gabbaygabbay120.00%
gabbayGABBAY!!!120.00%
gabbay?Gabbay?120.00%
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Mixed Types - Validation Dataset

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Search for various types of data in (a) column[s], including hidden mixes in strings.

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 col3
numbers20.00%
strings80.00%
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

String Mismatch - Validation Dataset

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Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

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  ValueCount% In data
Column NameBase form   
col1120.00%
#@$120.00%
nan#nan120.00%
nan120.00%
nanNan!120.00%
col2gabbaygabbay120.00%
gabbayGABBAY!!!120.00%
gabbay?Gabbay?120.00%
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Checks without condition

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Single Value in Column - Validation Dataset

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Check if there are columns which have only a single unique value in all rows.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "The following columns have only one unique value" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 col4
Single unique valuea
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Checks that passed condition

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Mixed Types - Validation Dataset

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Search for various types of data in (a) column[s], including hidden mixes in strings.

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 col3
numbers20.00%
strings80.00%
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "result = suite.run(validation_dataset=dataset)\n", + "result\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/drafts/date_leakage.ipynb b/examples/drafts/date_leakage.ipynb new file mode 100644 index 0000000000..84f654e23a --- /dev/null +++ b/examples/drafts/date_leakage.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7a16525a", + "metadata": {}, + "outputs": [], + "source": [ + "from mlchecks.checks.leakage import DateTrainTestLeakageDuplicates, DateTrainTestLeakageOverlap\n", + "from mlchecks.base import Dataset, Suite\n", + "from datetime import datetime\n", + "import pandas as pd\n", + "\n", + "def dataset_from_dict(d: dict, date_name: str = None) -> Dataset:\n", + " dataframe = pd.DataFrame(data=d)\n", + " return Dataset(dataframe, date=date_name)" + ] + }, + { + "cell_type": "markdown", + "id": "d80ca9b7-dc21-4edc-b8b5-b0186f5ff894", + "metadata": {}, + "source": [ + "## Synthetic example with date leakage" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2e089e04", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=2, style=ProgressStyle(bar_color='#9…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

date leakage

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Checks without condition

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Date Train-Validation Leakage (overlap)

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Check validation data that is dated earlier than latest date in train.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "27.3% of validation data dates before last training data date (2021-10-05 00:00:00)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Date Train-Validation Leakage (duplicates)

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Check if validation dates are present in train data.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "18.2% of validation data dates appear in training data" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 0
Sample of validation dates in train:[Timestamp('2021-10-05 00:00:00')]
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_ds = dataset_from_dict({'col1': [\n", + " datetime(2021, 10, 1, 0, 0),\n", + " datetime(2021, 10, 1, 0, 0),\n", + " datetime(2021, 10, 1, 0, 0),\n", + " datetime(2021, 10, 2, 0, 0),\n", + " datetime(2021, 10, 2, 0, 0),\n", + " datetime(2021, 10, 2, 0, 0),\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 5, 0, 0),\n", + " datetime(2021, 10, 5, 0, 0)\n", + " ]}, 'col1')\n", + "val_ds = dataset_from_dict({'col1': [\n", + " datetime(2021, 9, 4, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 5, 0, 0),\n", + " datetime(2021, 10, 6, 0, 0),\n", + " datetime(2021, 10, 6, 0, 0),\n", + " datetime(2021, 10, 7, 0, 0),\n", + " datetime(2021, 10, 7, 0, 0),\n", + " datetime(2021, 10, 8, 0, 0),\n", + " datetime(2021, 10, 8, 0, 0),\n", + " datetime(2021, 10, 9, 0, 0),\n", + " datetime(2021, 10, 9, 0, 0)\n", + " ]}, 'col1')\n", + "\n", + "suite = Suite(\"date leakage\", DateTrainTestLeakageOverlap(),\n", + " DateTrainTestLeakageDuplicates(n_to_show=1))\n", + "suite.run(train_dataset=train_ds, validation_dataset=val_ds)" + ] + }, + { + "cell_type": "markdown", + "id": "dbf03393", + "metadata": {}, + "source": [ + "## Synthetic example dates before last training" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "16feea0d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=2, style=ProgressStyle(bar_color='#9…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

date leakage

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Checks without condition

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Date Train-Validation Leakage (overlap)

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Check validation data that is dated earlier than latest date in train.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "11.1% of validation data dates before last training data date (2021-10-05 00:00:00)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Checks with nothing found

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Check
Date Train-Validation Leakage (duplicates)
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_ds = dataset_from_dict({'col1': [\n", + " datetime(2021, 10, 1, 0, 0),\n", + " datetime(2021, 10, 1, 0, 0),\n", + " datetime(2021, 10, 1, 0, 0),\n", + " datetime(2021, 10, 2, 0, 0),\n", + " datetime(2021, 10, 2, 0, 0),\n", + " datetime(2021, 10, 2, 0, 0),\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 5, 0, 0),\n", + " datetime(2021, 10, 5, 0, 0)\n", + " ]}, 'col1')\n", + "val_ds = dataset_from_dict({'col1': [\n", + " datetime(2021, 9, 4, 0, 0),\n", + " datetime(2021, 10, 6, 0, 0),\n", + " datetime(2021, 10, 6, 0, 0),\n", + " datetime(2021, 10, 7, 0, 0),\n", + " datetime(2021, 10, 7, 0, 0),\n", + " datetime(2021, 10, 8, 0, 0),\n", + " datetime(2021, 10, 8, 0, 0),\n", + " datetime(2021, 10, 9, 0, 0),\n", + " datetime(2021, 10, 9, 0, 0)\n", + " ]}, 'col1')\n", + "\n", + "suite = Suite(\"date leakage\", DateTrainTestLeakageOverlap(),\n", + " DateTrainTestLeakageDuplicates())\n", + "suite.run(train_dataset=train_ds, validation_dataset=val_ds)" + ] + }, + { + "cell_type": "markdown", + "id": "c1da7840", + "metadata": {}, + "source": [ + "## Synthetic example no date leakage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e1c5f401", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value=''), IntProgress(value=0, bar_style='info', max=2, style=ProgressStyle(bar_color='#9…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

date leakage

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Checks with nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Check
Date Train-Validation Leakage (overlap)
Date Train-Validation Leakage (duplicates)
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_ds = dataset_from_dict({'col1': [\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 3, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 4, 0, 0),\n", + " datetime(2021, 10, 5, 0, 0),\n", + " datetime(2021, 10, 5, 0, 0)\n", + " ]}, 'col1')\n", + "val_ds = dataset_from_dict({'col1': [\n", + " datetime(2021, 11, 4, 0, 0),\n", + " datetime(2021, 11, 4, 0, 0),\n", + " datetime(2021, 11, 5, 0, 0),\n", + " datetime(2021, 11, 6, 0, 0),\n", + "\n", + " ]}, 'col1')\n", + "\n", + "suite = Suite(\"date leakage\", DateTrainTestLeakageOverlap(),\n", + " DateTrainTestLeakageDuplicates())\n", + "suite.run(train_dataset=train_ds, validation_dataset=val_ds)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/drafts/suite_demonstration.ipynb b/examples/drafts/suite_demonstration.ipynb new file mode 100644 index 0000000000..888ab66e79 --- /dev/null +++ b/examples/drafts/suite_demonstration.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "aa401fe6", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e54802d4", + "metadata": {}, + "outputs": [], + "source": [ + "from mlchecks import Suite\n", + "from mlchecks.checks import *\n", + "\n", + "suite = Suite('My Single Suite',\n", + " IsSingleValue(),\n", + " MixedNulls(),\n", + " MixedTypes(),\n", + " StringMismatch()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "231d6218", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

My Single Suite

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Single Value in Column - Train Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Check if there are columns which have only a single unique value in all rows.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

✓ Nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Single Value in Column - Validation Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Check if there are columns which have only a single unique value in all rows.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "The following columns have only one unique value" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 target
Single unique value2
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Mixed Nulls - Train Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Search for various types of null values in a string column(s), including string representations of null.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

✓ Nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Mixed Nulls - Validation Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Search for various types of null values in a string column(s), including string representations of null.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

✓ Nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Mixed Types - Train Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Search for mixed types of Data in a single column[s].

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

✓ Nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Mixed Types - Validation Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Search for mixed types of Data in a single column[s].

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

✓ Nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

String Mismatch - Train Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

✓ Nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

String Mismatch - Validation Dataset

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Detect different variants of string categories (e.g. \"mislabeled\" vs \"mis-labeled\") in a categorical column.

" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

✓ Nothing found

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "\n", + "df = load_iris(return_X_y=False, as_frame=True)\n", + "df = pd.concat([df.data, df.target], axis=1)\n", + "\n", + "train = df[:100]\n", + "val = df[100:]\n", + "\n", + "suite.run(train_dataset=train, validation_dataset=val, check_datasets_policy='both')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}