diff --git a/docs/DOCSMAIN.md b/docs/DOCSMAIN.md index 2c34026..3b13f0b 100644 --- a/docs/DOCSMAIN.md +++ b/docs/DOCSMAIN.md @@ -30,6 +30,6 @@ outrank --help # Example use cases -* A minimal showcase of performing feature ranking on a generic CSV is demonstrated with [this example](https://github.com/outbrain/outrank/tree/main/scripts) +* A minimal showcase of performing feature ranking on a generic CSV is demonstrated with [this example](https://github.com/outbrain/outrank/tree/main/scripts). * [More examples](https://github.com/outbrain/outrank/tree/main/examples) demonstrating OutRank's capabilities are also available. diff --git a/docs/outrank.html b/docs/outrank.html index 084cb2f..2e70486 100644 --- a/docs/outrank.html +++ b/docs/outrank.html @@ -93,7 +93,7 @@

Setup

Example use cases

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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oWelcome to OutRank's documentation!\n\n

All functions/methods can be searched-for (search bar on the left).

\n\n

This tool enables fast screening of feature-feature interactions. Its purpose is to give the user fast insight into potential redundancies/anomalies in the data.\nIt is implemented to operate in _mini batches_, it traverses the raw data incrementally, refining the rankings as it goes along. The core operation, interaction ranking, outputs triplets which look as follows:

\n\n
featureA    featureB    0.512\nfeatureA    featureC    0.125\n
\n\n

Setup

\n\n
\n
pip install outrank\n
\n
\n\n

and test a minimal cycle with

\n\n
\n
outrank --task selftest\n
\n
\n\n

if this passes, you can be pretty certain OutRank will perform as intended. OutRank's primary use case is as a CLI tool, begin exploring with

\n\n
\n
outrank --help\n
\n
\n\n

Example use cases

\n\n
    \n

  • A minimal showcase of performing feature ranking on a generic CSV is demonstrated with this example

    • \n

  • More examples demonstrating OutRank's capabilities are also available.

    • \n
\n"}, "outrank.algorithms": {"fullname": "outrank.algorithms", "modulename": "outrank.algorithms", "kind": "module", "doc": "

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\n"}, "outrank.algorithms.feature_ranking.ranking_mi_numba.numba_unique": {"fullname": "outrank.algorithms.feature_ranking.ranking_mi_numba.numba_unique", "modulename": "outrank.algorithms.feature_ranking.ranking_mi_numba", "qualname": "numba_unique", "kind": "function", "doc": "

Identify unique elements in an array, fast

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Core entropy computation function

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Core estimator logic. Compute unique elements, subset if required

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\n", "signature": "(vector_first: Any, vector_second: Any, surrogate_model: str) -> float:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.numba_mi": {"fullname": "outrank.algorithms.importance_estimator.numba_mi", "modulename": "outrank.algorithms.importance_estimator", "qualname": "numba_mi", "kind": "function", "doc": "

\n", "signature": "(vector_first, vector_second, heuristic):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.sklearn_mi_adj": {"fullname": "outrank.algorithms.importance_estimator.sklearn_mi_adj", "modulename": "outrank.algorithms.importance_estimator", "qualname": "sklearn_mi_adj", "kind": "function", "doc": "

\n", "signature": "(vector_first, vector_second):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.get_importances_estimate_pairwise": {"fullname": "outrank.algorithms.importance_estimator.get_importances_estimate_pairwise", "modulename": "outrank.algorithms.importance_estimator", "qualname": "get_importances_estimate_pairwise", "kind": "function", "doc": "

A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel.

\n", "signature": "(combination, args, tmp_df):", "funcdef": "def"}, "outrank.algorithms.importance_estimator.rank_features_3MR": {"fullname": "outrank.algorithms.importance_estimator.rank_features_3MR", "modulename": "outrank.algorithms.importance_estimator", "qualname": "rank_features_3MR", "kind": "function", "doc": "

\n", "signature": "(\trelevance_dict: dict[str, float],\tredundancy_dict: dict[tuple[typing.Any, typing.Any], typing.Any],\trelational_dict: dict[tuple[typing.Any, typing.Any], typing.Any],\tstrategy: str = 'median',\talpha: float = 1,\tbeta: float = 1) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.algorithms.importance_estimator.get_importances_estimate_nonmyopic": {"fullname": "outrank.algorithms.importance_estimator.get_importances_estimate_nonmyopic", "modulename": "outrank.algorithms.importance_estimator", "qualname": "get_importances_estimate_nonmyopic", "kind": "function", "doc": "

\n", "signature": "(args: Any, tmp_df: pandas.core.frame.DataFrame):", "funcdef": "def"}, "outrank.algorithms.sketches": {"fullname": "outrank.algorithms.sketches", "modulename": "outrank.algorithms.sketches", "kind": "module", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "kind": "module", "doc": "

This module implements probabilistic data structure which is able to calculate the cardinality of large multisets in a single pass using little auxiliary memory

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache", "kind": "class", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.__init__": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.__init__", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.__init__", "kind": "function", "doc": "

\n", "signature": "(error_rate=0.005)"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.p": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.p", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.p", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.m": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.m", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.m", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_set": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_set", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.warmup_set", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_size": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.warmup_size", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.warmup_size", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.width": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.width", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.width", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.hll_flag": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.hll_flag", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.hll_flag", "kind": "variable", "doc": "

\n"}, "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.add": {"fullname": "outrank.algorithms.sketches.counting_ultiloglog.HyperLogLogWCache.add", "modulename": "outrank.algorithms.sketches.counting_ultiloglog", "qualname": "HyperLogLogWCache.add", "kind": "function", "doc": "

\n", "signature": "(self, value):", "funcdef": "def"}, "outrank.algorithms.synthetic_data_generators": {"fullname": "outrank.algorithms.synthetic_data_generators", "modulename": "outrank.algorithms.synthetic_data_generators", "kind": "module", "doc": "

\n"}, "outrank.algorithms.synthetic_data_generators.generator_naive": {"fullname": "outrank.algorithms.synthetic_data_generators.generator_naive", "modulename": "outrank.algorithms.synthetic_data_generators.generator_naive", "kind": "module", "doc": "

\n"}, "outrank.algorithms.synthetic_data_generators.generator_naive.generate_random_matrix": {"fullname": "outrank.algorithms.synthetic_data_generators.generator_naive.generate_random_matrix", "modulename": "outrank.algorithms.synthetic_data_generators.generator_naive", "qualname": "generate_random_matrix", "kind": "function", "doc": "

\n", "signature": "(num_features=100, size=20000):", "funcdef": "def"}, "outrank.core_ranking": {"fullname": "outrank.core_ranking", "modulename": "outrank.core_ranking", "kind": "module", "doc": "

\n"}, "outrank.core_ranking.logger": {"fullname": "outrank.core_ranking.logger", "modulename": "outrank.core_ranking", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.core_ranking.GLOBAL_CARDINALITY_STORAGE": {"fullname": "outrank.core_ranking.GLOBAL_CARDINALITY_STORAGE", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_CARDINALITY_STORAGE", "kind": "variable", "doc": "

\n", "annotation": ": dict[typing.Any, typing.Any]", "default_value": "{}"}, "outrank.core_ranking.GLOBAL_RARE_VALUE_STORAGE": {"fullname": "outrank.core_ranking.GLOBAL_RARE_VALUE_STORAGE", "modulename": "outrank.core_ranking", "qualname": "GLOBAL_RARE_VALUE_STORAGE", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, typing.Any]", "default_value": "Counter()"}, "outrank.core_ranking.IGNORED_VALUES": {"fullname": "outrank.core_ranking.IGNORED_VALUES", "modulename": "outrank.core_ranking", "qualname": "IGNORED_VALUES", "kind": "variable", "doc": "

\n", "default_value": "set()"}, "outrank.core_ranking.HYPERLL_ERROR_BOUND": {"fullname": "outrank.core_ranking.HYPERLL_ERROR_BOUND", "modulename": "outrank.core_ranking", "qualname": "HYPERLL_ERROR_BOUND", "kind": "variable", "doc": "

\n", "default_value": "0.02"}, "outrank.core_ranking.encode_int_column": {"fullname": "outrank.core_ranking.encode_int_column", "modulename": "outrank.core_ranking", "qualname": "encode_int_column", "kind": "function", "doc": "

Encode column values as categoric (at a batch level!)

\n", "signature": "(input_tuple: tuple[str, typing.Any]) -> tuple[typing.Any, list[int]]:", "funcdef": "def"}, "outrank.core_ranking.mixed_rank_graph": {"fullname": "outrank.core_ranking.mixed_rank_graph", "modulename": "outrank.core_ranking", "qualname": "mixed_rank_graph", "kind": "function", "doc": "

Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any,\tcpu_pool: Any,\tpbar: Any) -> outrank.core_utils.BatchRankingSummary:", "funcdef": "def"}, "outrank.core_ranking.enrich_with_transformations": {"fullname": "outrank.core_ranking.enrich_with_transformations", "modulename": "outrank.core_ranking", "qualname": "enrich_with_transformations", "kind": "function", "doc": "

Construct a collection of new features based on pre-defined transformations/rules

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tnum_col_types: set[str],\tlogger: Any,\targs: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_combined_features": {"fullname": "outrank.core_ranking.compute_combined_features", "modulename": "outrank.core_ranking", "qualname": "compute_combined_features", "kind": "function", "doc": "

Compute higher order features via xxhash-based trick.

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any,\tis_3mr: bool = False) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_expanded_multivalue_features": {"fullname": "outrank.core_ranking.compute_expanded_multivalue_features", "modulename": "outrank.core_ranking", "qualname": "compute_expanded_multivalue_features", "kind": "function", "doc": "

Compute one-hot encoded feature space based on each designated multivalue feature. E.g., feature with value \"a,b,c\" becomes three features, values of which are presence of a given value in a mutlivalue feature of choice.

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_subfeatures": {"fullname": "outrank.core_ranking.compute_subfeatures", "modulename": "outrank.core_ranking", "qualname": "compute_subfeatures", "kind": "function", "doc": "

Compute derived features that are more fine-grained. Implements logic around two operators that govern feature construction.\n->: One sided construction - every value from left side is fine, separate ones from the right side feature will be considered.\n<->: Two sided construction - two-sided values present. This means that each value from a is combined with each from b, forming |A|*|B| new features (one-hot encoded)

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any,\tpbar: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.include_noisy_features": {"fullname": "outrank.core_ranking.include_noisy_features", "modulename": "outrank.core_ranking", "qualname": "include_noisy_features", "kind": "function", "doc": "

Add randomized features that serve as a sanity check

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tlogger: Any,\targs: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.compute_coverage": {"fullname": "outrank.core_ranking.compute_coverage", "modulename": "outrank.core_ranking", "qualname": "compute_coverage", "kind": "function", "doc": "

Compute coverage of features, incrementally

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_feature_memory_consumption": {"fullname": "outrank.core_ranking.compute_feature_memory_consumption", "modulename": "outrank.core_ranking", "qualname": "compute_feature_memory_consumption", "kind": "function", "doc": "

An approximation of how much feature take up

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_value_counts": {"fullname": "outrank.core_ranking.compute_value_counts", "modulename": "outrank.core_ranking", "qualname": "compute_value_counts", "kind": "function", "doc": "

Update the count structure

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, args: Any):", "funcdef": "def"}, "outrank.core_ranking.compute_cardinalities": {"fullname": "outrank.core_ranking.compute_cardinalities", "modulename": "outrank.core_ranking", "qualname": "compute_cardinalities", "kind": "function", "doc": "

Compute cardinalities of features, incrementally

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, pbar: Any) -> None:", "funcdef": "def"}, "outrank.core_ranking.compute_bounds_increment": {"fullname": "outrank.core_ranking.compute_bounds_increment", "modulename": "outrank.core_ranking", "qualname": "compute_bounds_increment", "kind": "function", "doc": "

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tnumeric_column_types: set[str]) -> dict[str, typing.Any]:", "funcdef": "def"}, "outrank.core_ranking.compute_batch_ranking": {"fullname": "outrank.core_ranking.compute_batch_ranking", "modulename": "outrank.core_ranking", "qualname": "compute_batch_ranking", "kind": "function", "doc": "

Enrich the feature space and compute the batch importances

\n", "signature": "(\tline_tmp_storage: list[list[typing.Any]],\tnumeric_column_types: set[str],\targs: Any,\tcpu_pool: Any,\tcolumn_descriptions: list[str],\tlogger: Any,\tpbar: Any) -> tuple[outrank.core_utils.BatchRankingSummary, dict[str, typing.Any], dict[str, set[str]], dict[str, set[str]]]:", "funcdef": "def"}, "outrank.core_ranking.get_num_of_instances": {"fullname": "outrank.core_ranking.get_num_of_instances", "modulename": "outrank.core_ranking", "qualname": "get_num_of_instances", "kind": "function", "doc": "

Count the number of lines in a file, fast - useful for progress logging

\n", "signature": "(fname: str) -> int:", "funcdef": "def"}, "outrank.core_ranking.get_grouped_df": {"fullname": "outrank.core_ranking.get_grouped_df", "modulename": "outrank.core_ranking", "qualname": "get_grouped_df", "kind": "function", "doc": "

A helper method that enables median-based aggregation after processing

\n", "signature": "(\timportances_df_list: list[tuple[str, str, float]]) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.checkpoint_importances_df": {"fullname": "outrank.core_ranking.checkpoint_importances_df", "modulename": "outrank.core_ranking", "qualname": "checkpoint_importances_df", "kind": "function", "doc": "

A helper which stores intermediary state - useful for longer runs

\n", "signature": "(importances_batch: list[tuple[str, str, float]]) -> None:", "funcdef": "def"}, "outrank.core_ranking.estimate_importances_minibatches": {"fullname": "outrank.core_ranking.estimate_importances_minibatches", "modulename": "outrank.core_ranking", "qualname": "estimate_importances_minibatches", "kind": "function", "doc": "

Interaction score estimator - suitable for example for csv-like input data types.\nThis type of data is normally a single large csv, meaning that minibatch processing needs to\nhappen during incremental handling of the file (that\"s not the case for pre-separated ob data)

\n", "signature": "(\tinput_file: str,\tcolumn_descriptions: list,\tfw_col_mapping: dict[str, str],\tnumeric_column_types: set,\tbatch_size: int = 100000,\targs: Any = None,\tdata_encoding: str = 'utf-8',\tcpu_pool: Any = None,\tdelimiter: str = '\\t',\tfeature_construction_mode: bool = False,\tlogger: Any = None) -> tuple[list[dict[str, typing.Any]], typing.Any, dict[typing.Any, typing.Any], list[dict[str, typing.Any]], list[dict[str, set[str]]], collections.defaultdict[str, list[set[str]]], dict[str, typing.Any]]:", "funcdef": "def"}, "outrank.core_selftest": {"fullname": "outrank.core_selftest", "modulename": "outrank.core_selftest", "kind": "module", "doc": "

\n"}, "outrank.core_utils": {"fullname": "outrank.core_utils", "modulename": "outrank.core_utils", "kind": "module", "doc": "

\n"}, "outrank.core_utils.pro_tips": {"fullname": "outrank.core_utils.pro_tips", "modulename": "outrank.core_utils", "qualname": "pro_tips", "kind": "variable", "doc": "

\n", "default_value": "['OutRank can construct subfeatures; features based on subspaces. Example command argument is: --subfeature_mapping "feature_a->feature_b;feature_c<->feature_d;feature_c<->feature_e"', 'Heuristic MI-numba-randomized seems like the best of both worlds! (speed + performance).', 'Heuristic surrogate-lr performs cross-validation (internally), keep that in mind!', 'Consider running OutRank on a smaller data sample first, might be enough (--subsampling = a lot).', 'There are two types of combinations supported; unsupervised pairwise ranking (redundancies- --target_ranking_only=False), and supervised combinations - (--interaction_order > 1)', 'Visualization part also includes clustering - this might be very insightful!', 'By default OutRank includes feature cardinality and coverage in feature names (card; cov)', 'Intermediary checkpoints (tmp_checkpoint.tsv) might already give you insights during longer runs.', 'In theory, you can rank redundancies of combined features (--interaction_order AND --target_ranking_only=False).', 'Give it as many threads as physically possible (--num_threads).', 'You can speed up ranking by diminishing feature buffer size (--combination_number_upper_bound determines how many ranking computations per batch will be considered). This, and --subsampling are very powerful together.', 'Want to rank feature transformations, but not sure which ones to choose? --transformers=default should serve as a solid baseline (common DS transformations included).', 'Your target can be any feature! (explaining one feature with others)', 'OutRank uses HyperLogLog for cardinality estimation - this is also a potential usecase (understanding cardinalities across different data sets).', 'Each feature is named as featureName(cardinality, coverage in percents) in the final files.', 'You can generate candidate feature transformation ranges (fw) by using --task=feature_summary_transformers.']"}, "outrank.core_utils.write_json_dump_to_file": {"fullname": "outrank.core_utils.write_json_dump_to_file", "modulename": "outrank.core_utils", "qualname": "write_json_dump_to_file", "kind": "function", "doc": "

\n", "signature": "(args: Any, config_name: str) -> None:", "funcdef": "def"}, "outrank.core_utils.internal_hash": {"fullname": "outrank.core_utils.internal_hash", "modulename": "outrank.core_utils", "qualname": "internal_hash", "kind": "function", "doc": "

A generic internal hash used throughout ranking procedure - let's hardcode seed here for sure

\n", "signature": "(input_obj: str) -> str:", "funcdef": "def"}, "outrank.core_utils.DatasetInformationStorage": {"fullname": "outrank.core_utils.DatasetInformationStorage", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage", "kind": "class", "doc": "

A generic class for holding properties of a given type of dataset

\n"}, "outrank.core_utils.DatasetInformationStorage.__init__": {"fullname": "outrank.core_utils.DatasetInformationStorage.__init__", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.__init__", "kind": "function", "doc": "

\n", "signature": "(\tdata_path: str,\tcolumn_names: list[str],\tcolumn_types: set[str],\tcol_delimiter: str | None,\tencoding: str,\tfw_map: dict[str, str] | None)"}, "outrank.core_utils.DatasetInformationStorage.data_path": {"fullname": "outrank.core_utils.DatasetInformationStorage.data_path", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.data_path", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.column_names": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_names", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_names", "kind": "variable", "doc": "

\n", "annotation": ": list[str]"}, "outrank.core_utils.DatasetInformationStorage.column_types": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_types", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_types", "kind": "variable", "doc": "

\n", "annotation": ": set[str]"}, "outrank.core_utils.DatasetInformationStorage.col_delimiter": {"fullname": "outrank.core_utils.DatasetInformationStorage.col_delimiter", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.col_delimiter", "kind": "variable", "doc": "

\n", "annotation": ": str | None"}, "outrank.core_utils.DatasetInformationStorage.encoding": {"fullname": "outrank.core_utils.DatasetInformationStorage.encoding", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.encoding", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.fw_map": {"fullname": "outrank.core_utils.DatasetInformationStorage.fw_map", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.fw_map", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, str] | None"}, "outrank.core_utils.NumericFeatureSummary": {"fullname": "outrank.core_utils.NumericFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NumericFeatureSummary.__init__": {"fullname": "outrank.core_utils.NumericFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\tfeature_name: str,\tminimum: float,\tmaximum: float,\tmedian: float,\tnum_unique: int)"}, "outrank.core_utils.NumericFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NumericFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NumericFeatureSummary.minimum": {"fullname": "outrank.core_utils.NumericFeatureSummary.minimum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.minimum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.maximum": {"fullname": "outrank.core_utils.NumericFeatureSummary.maximum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.maximum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.median": {"fullname": "outrank.core_utils.NumericFeatureSummary.median", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.median", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NumericFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.NominalFeatureSummary": {"fullname": "outrank.core_utils.NominalFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NominalFeatureSummary.__init__": {"fullname": "outrank.core_utils.NominalFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(feature_name: str, num_unique: int)"}, "outrank.core_utils.NominalFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NominalFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NominalFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NominalFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.BatchRankingSummary": {"fullname": "outrank.core_utils.BatchRankingSummary", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary", "kind": "class", "doc": "

A generic class representing batched ranking results

\n"}, "outrank.core_utils.BatchRankingSummary.__init__": {"fullname": "outrank.core_utils.BatchRankingSummary.__init__", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttriplet_scores: list[tuple[str, str, float]],\tstep_times: dict[str, typing.Any])"}, "outrank.core_utils.BatchRankingSummary.triplet_scores": {"fullname": "outrank.core_utils.BatchRankingSummary.triplet_scores", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.triplet_scores", "kind": "variable", "doc": "

\n", "annotation": ": list[tuple[str, str, float]]"}, "outrank.core_utils.BatchRankingSummary.step_times": {"fullname": "outrank.core_utils.BatchRankingSummary.step_times", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.step_times", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, typing.Any]"}, "outrank.core_utils.display_random_tip": {"fullname": "outrank.core_utils.display_random_tip", "modulename": "outrank.core_utils", "qualname": "display_random_tip", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.get_dataset_info": {"fullname": "outrank.core_utils.get_dataset_info", "modulename": "outrank.core_utils", "qualname": "get_dataset_info", "kind": "function", "doc": "

\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.core_utils.display_tool_name": {"fullname": "outrank.core_utils.display_tool_name", "modulename": "outrank.core_utils", "qualname": "display_tool_name", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line": {"fullname": "outrank.core_utils.parse_ob_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_line", "kind": "function", "doc": "

Outbrain line parsing - generic TSVs

\n", "signature": "(line_string: str, delimiter: str = '\\t', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line_vw": {"fullname": "outrank.core_utils.parse_ob_line_vw", "modulename": "outrank.core_utils", "qualname": "parse_ob_line_vw", "kind": "function", "doc": "

Parse a sparse vw line into a pandas df with pre-defined namespace

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping=None,\ttable_header=None,\tinclude_namespace_info=False) -> list[str | None]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_csv_line": {"fullname": "outrank.core_utils.parse_ob_csv_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_csv_line", "kind": "function", "doc": "

Data can have commas within JSON field dumps

\n", "signature": "(line_string: str, delimiter: str = ',', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.generic_line_parser": {"fullname": "outrank.core_utils.generic_line_parser", "modulename": "outrank.core_utils", "qualname": "generic_line_parser", "kind": "function", "doc": "

A generic method aimed to parse data from different sources.

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping: Any = None,\ttable_header: Any = None) -> list[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.read_reference_json": {"fullname": "outrank.core_utils.read_reference_json", "modulename": "outrank.core_utils", "qualname": "read_reference_json", "kind": "function", "doc": "

A helper method for reading a JSON

\n", "signature": "(json_path) -> dict[str, dict]:", "funcdef": "def"}, "outrank.core_utils.parse_namespace": {"fullname": "outrank.core_utils.parse_namespace", "modulename": "outrank.core_utils", "qualname": "parse_namespace", "kind": "function", "doc": "

Parse the feature namespace for type awareness

\n", "signature": "(namespace_path: str) -> tuple[set[str], dict[str, str]]:", "funcdef": "def"}, "outrank.core_utils.read_column_names": {"fullname": "outrank.core_utils.read_column_names", "modulename": "outrank.core_utils", "qualname": "read_column_names", "kind": "function", "doc": "

Read the col. header

\n", "signature": "(mapping_file: str) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_vw_feature_information": {"fullname": "outrank.core_utils.parse_ob_vw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_vw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_raw_feature_information": {"fullname": "outrank.core_utils.parse_ob_raw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_raw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_feature_information": {"fullname": "outrank.core_utils.parse_ob_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_with_description_information": {"fullname": "outrank.core_utils.parse_csv_with_description_information", "modulename": "outrank.core_utils", "qualname": "parse_csv_with_description_information", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_raw": {"fullname": "outrank.core_utils.parse_csv_raw", "modulename": "outrank.core_utils", "qualname": "parse_csv_raw", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.extract_features_from_reference_JSON": {"fullname": "outrank.core_utils.extract_features_from_reference_JSON", "modulename": "outrank.core_utils", "qualname": "extract_features_from_reference_JSON", "kind": "function", "doc": "

Given a model's JSON, extract unique features

\n", "signature": "(json_path: str) -> set[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.summarize_feature_bounds_for_transformers": {"fullname": "outrank.core_utils.summarize_feature_bounds_for_transformers", "modulename": "outrank.core_utils", "qualname": "summarize_feature_bounds_for_transformers", "kind": "function", "doc": "

summarization auxilliary method for generating JSON-based specs

\n", "signature": "(\tbounds_object_storage: Any,\tfeature_types: list[str],\ttask_name: str,\tlabel_name: str,\tgranularity: int = 15,\toutput_summary_table_only: bool = False):", "funcdef": "def"}, "outrank.core_utils.summarize_rare_counts": {"fullname": "outrank.core_utils.summarize_rare_counts", "modulename": "outrank.core_utils", "qualname": "summarize_rare_counts", "kind": "function", "doc": "

Write rare values

\n", "signature": "(\tterm_counter: Any,\targs: Any,\tcardinality_object: Any,\tobject_info: outrank.core_utils.DatasetInformationStorage) -> None:", "funcdef": "def"}, "outrank.feature_transformations": {"fullname": "outrank.feature_transformations", "modulename": "outrank.feature_transformations", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault": {"fullname": "outrank.feature_transformations.feature_transformer_vault", "modulename": "outrank.feature_transformations.feature_transformer_vault", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "MINIMAL_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)'}"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "DEFAULT_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)'}"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "FW_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)', '_tr_fw_sqrt_res_1_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*1,0), 0))', '_tr_fw_log_res_1_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*1,0), 0))', '_tr_fw_log_res_1_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*1,0), 0))', '_tr_fw_log_res_1_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*1,0), 0))', '_tr_fw_log_res_1_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*1,0), 0))', '_tr_fw_log_res_1_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*1,0), 0))', '_tr_fw_log_res_1_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*1,0), 0))', '_tr_fw_log_res_1_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*1,0), 0))', '_tr_fw_log_res_1_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*1,0), 0))', '_tr_fw_sqrt_res_10_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*10,0), 0))', '_tr_fw_log_res_10_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*10,0), 0))', '_tr_fw_log_res_10_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*10,0), 0))', '_tr_fw_log_res_10_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*10,0), 0))', '_tr_fw_log_res_10_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*10,0), 0))', '_tr_fw_log_res_10_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*10,0), 0))', '_tr_fw_log_res_10_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*10,0), 0))', '_tr_fw_log_res_10_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*10,0), 0))', '_tr_fw_log_res_10_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*10,0), 0))', '_tr_fw_sqrt_res_50_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*50,0), 0))', '_tr_fw_log_res_50_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*50,0), 0))', '_tr_fw_log_res_50_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*50,0), 0))', '_tr_fw_log_res_50_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*50,0), 0))', '_tr_fw_log_res_50_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*50,0), 0))', '_tr_fw_log_res_50_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*50,0), 0))', '_tr_fw_log_res_50_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*50,0), 0))', '_tr_fw_log_res_50_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*50,0), 0))', '_tr_fw_log_res_50_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*50,0), 0))', '_tr_fw_sqrt_res_100_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*100,0), 0))', '_tr_fw_log_res_100_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*100,0), 0))', '_tr_fw_log_res_100_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*100,0), 0))', '_tr_fw_log_res_100_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*100,0), 0))', '_tr_fw_log_res_100_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*100,0), 0))', '_tr_fw_log_res_100_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*100,0), 0))', '_tr_fw_log_res_100_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*100,0), 0))', '_tr_fw_log_res_100_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*100,0), 0))', '_tr_fw_log_res_100_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*100,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*1,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*10,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*50,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.08': 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\n", "default_value": "[1, 10, 50, 100]"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "greater_than_range", "kind": "variable", "doc": "

\n", "default_value": "[1, 2, 4, 8, 16, 32, 64, 96]"}, "outrank.feature_transformations.ranking_transformers": {"fullname": "outrank.feature_transformations.ranking_transformers", "modulename": "outrank.feature_transformations.ranking_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise", "kind": "class", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.noise_preset": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.noise_preset", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise.noise_preset", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.construct_new_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise.construct_new_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise.construct_new_features", "kind": "function", "doc": "

Generate a few standard noise distributions

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\n", "annotation": ": set[str]"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.max_maj_support": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.max_maj_support", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.max_maj_support", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.nan_prop_support": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.nan_prop_support", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.nan_prop_support", "kind": "variable", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.get_vals": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.get_vals", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.get_vals", "kind": "function", "doc": "

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\n", "signature": "(self, dataframe: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_new_features": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerGeneric.construct_new_features", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerGeneric.construct_new_features", "kind": "function", "doc": "

\n", "signature": "(self, dataframe: Any) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.task_generators": {"fullname": "outrank.task_generators", "modulename": "outrank.task_generators", "kind": "module", "doc": "

\n"}, "outrank.task_generators.logger": {"fullname": "outrank.task_generators.logger", "modulename": "outrank.task_generators", "qualname": "logger", "kind": "variable", "doc": "

\n", "default_value": "<Logger syn-logger (DEBUG)>"}, "outrank.task_generators.outrank_task_generate_data_set": {"fullname": "outrank.task_generators.outrank_task_generate_data_set", "modulename": "outrank.task_generators", "qualname": "outrank_task_generate_data_set", "kind": "function", "doc": "

Core method for generating data sets

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\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.task_selftest": {"fullname": "outrank.task_selftest", "modulename": "outrank.task_selftest", "kind": "module", "doc": "

\n"}, "outrank.task_selftest.logger": {"fullname": "outrank.task_selftest.logger", "modulename": "outrank.task_selftest", "qualname": "logger", "kind": "variable", "doc": "

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\n", "signature": "():", "funcdef": "def"}, "outrank.task_summary": {"fullname": "outrank.task_summary", "modulename": "outrank.task_summary", "kind": "module", "doc": "

\n"}, "outrank.task_summary.outrank_task_result_summary": {"fullname": "outrank.task_summary.outrank_task_result_summary", "modulename": "outrank.task_summary", "qualname": "outrank_task_result_summary", "kind": "function", "doc": "

\n", "signature": "(args):", "funcdef": "def"}, "outrank.task_visualization": {"fullname": "outrank.task_visualization", "modulename": "outrank.task_visualization", "kind": "module", "doc": "

\n"}, "outrank.task_visualization.outrank_task_visualize_results": {"fullname": "outrank.task_visualization.outrank_task_visualize_results", "modulename": "outrank.task_visualization", "qualname": "outrank_task_visualize_results", "kind": "function", "doc": "

\n", "signature": "(args):", "funcdef": "def"}, "outrank.visualizations": {"fullname": "outrank.visualizations", "modulename": "outrank.visualizations", "kind": "module", "doc": "

\n"}, "outrank.visualizations.ranking_visualization": {"fullname": "outrank.visualizations.ranking_visualization", "modulename": "outrank.visualizations.ranking_visualization", "kind": "module", "doc": "

\n"}, "outrank.visualizations.ranking_visualization.visualize_hierarchical_clusters": {"fullname": "outrank.visualizations.ranking_visualization.visualize_hierarchical_clusters", "modulename": "outrank.visualizations.ranking_visualization", "qualname": "visualize_hierarchical_clusters", "kind": "function", "doc": "

A method for visualization of hierarchical clusters w.r.t. different linkage functions

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A method for visualization of the obtained feature interaction maps.

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Welcome to OutRank's documentation!

\n\n

All functions/methods can be searched-for (search bar on the left).

\n\n

This tool enables fast screening of feature-feature interactions. Its purpose is to give the user fast insight into potential redundancies/anomalies in the data.\nIt is implemented to operate in _mini batches_, it traverses the raw data incrementally, refining the rankings as it goes along. The core operation, interaction ranking, outputs triplets which look as follows:

\n\n
featureA    featureB    0.512\nfeatureA    featureC    0.125\n
\n\n

Setup

\n\n
\n
pip install outrank\n
\n
\n\n

and test a minimal cycle with

\n\n
\n
outrank --task selftest\n
\n
\n\n

if this passes, you can be pretty certain OutRank will perform as intended. OutRank's primary use case is as a CLI tool, begin exploring with

\n\n
\n
outrank --help\n
\n
\n\n

Example use cases

\n\n
    \n

  • A minimal showcase of performing feature ranking on a generic CSV is demonstrated with this example.

    • \n

  • More examples demonstrating OutRank's capabilities are also available.

    • \n
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Identify unique elements in an array, fast

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Core entropy computation function

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Core estimator logic. Compute unique elements, subset if required

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A method for parallel importances estimation. As interaction scoring is independent, individual scores can be computed in parallel.

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This module implements probabilistic data structure which is able to calculate the cardinality of large multisets in a single pass using little auxiliary memory

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Compute the full mixed rank graph corresponding to all pairwise feature interactions based on the selected heuristic

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Construct a collection of new features based on pre-defined transformations/rules

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Compute higher order features via xxhash-based trick.

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Compute one-hot encoded feature space based on each designated multivalue feature. E.g., feature with value \"a,b,c\" becomes three features, values of which are presence of a given value in a mutlivalue feature of choice.

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Add randomized features that serve as a sanity check

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Compute coverage of features, incrementally

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_feature_memory_consumption": {"fullname": "outrank.core_ranking.compute_feature_memory_consumption", "modulename": "outrank.core_ranking", "qualname": "compute_feature_memory_consumption", "kind": "function", "doc": "

An approximation of how much feature take up

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\targs: Any) -> dict[str, set[str]]:", "funcdef": "def"}, "outrank.core_ranking.compute_value_counts": {"fullname": "outrank.core_ranking.compute_value_counts", "modulename": "outrank.core_ranking", "qualname": "compute_value_counts", "kind": "function", "doc": "

Update the count structure

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, args: Any):", "funcdef": "def"}, "outrank.core_ranking.compute_cardinalities": {"fullname": "outrank.core_ranking.compute_cardinalities", "modulename": "outrank.core_ranking", "qualname": "compute_cardinalities", "kind": "function", "doc": "

Compute cardinalities of features, incrementally

\n", "signature": "(input_dataframe: pandas.core.frame.DataFrame, pbar: Any) -> None:", "funcdef": "def"}, "outrank.core_ranking.compute_bounds_increment": {"fullname": "outrank.core_ranking.compute_bounds_increment", "modulename": "outrank.core_ranking", "qualname": "compute_bounds_increment", "kind": "function", "doc": "

\n", "signature": "(\tinput_dataframe: pandas.core.frame.DataFrame,\tnumeric_column_types: set[str]) -> dict[str, typing.Any]:", "funcdef": "def"}, "outrank.core_ranking.compute_batch_ranking": {"fullname": "outrank.core_ranking.compute_batch_ranking", "modulename": "outrank.core_ranking", "qualname": "compute_batch_ranking", "kind": "function", "doc": "

Enrich the feature space and compute the batch importances

\n", "signature": "(\tline_tmp_storage: list[list[typing.Any]],\tnumeric_column_types: set[str],\targs: Any,\tcpu_pool: Any,\tcolumn_descriptions: list[str],\tlogger: Any,\tpbar: Any) -> tuple[outrank.core_utils.BatchRankingSummary, dict[str, typing.Any], dict[str, set[str]], dict[str, set[str]]]:", "funcdef": "def"}, "outrank.core_ranking.get_num_of_instances": {"fullname": "outrank.core_ranking.get_num_of_instances", "modulename": "outrank.core_ranking", "qualname": "get_num_of_instances", "kind": "function", "doc": "

Count the number of lines in a file, fast - useful for progress logging

\n", "signature": "(fname: str) -> int:", "funcdef": "def"}, "outrank.core_ranking.get_grouped_df": {"fullname": "outrank.core_ranking.get_grouped_df", "modulename": "outrank.core_ranking", "qualname": "get_grouped_df", "kind": "function", "doc": "

A helper method that enables median-based aggregation after processing

\n", "signature": "(\timportances_df_list: list[tuple[str, str, float]]) -> pandas.core.frame.DataFrame:", "funcdef": "def"}, "outrank.core_ranking.checkpoint_importances_df": {"fullname": "outrank.core_ranking.checkpoint_importances_df", "modulename": "outrank.core_ranking", "qualname": "checkpoint_importances_df", "kind": "function", "doc": "

A helper which stores intermediary state - useful for longer runs

\n", "signature": "(importances_batch: list[tuple[str, str, float]]) -> None:", "funcdef": "def"}, "outrank.core_ranking.estimate_importances_minibatches": {"fullname": "outrank.core_ranking.estimate_importances_minibatches", "modulename": "outrank.core_ranking", "qualname": "estimate_importances_minibatches", "kind": "function", "doc": "

Interaction score estimator - suitable for example for csv-like input data types.\nThis type of data is normally a single large csv, meaning that minibatch processing needs to\nhappen during incremental handling of the file (that\"s not the case for pre-separated ob data)

\n", "signature": "(\tinput_file: str,\tcolumn_descriptions: list,\tfw_col_mapping: dict[str, str],\tnumeric_column_types: set,\tbatch_size: int = 100000,\targs: Any = None,\tdata_encoding: str = 'utf-8',\tcpu_pool: Any = None,\tdelimiter: str = '\\t',\tfeature_construction_mode: bool = False,\tlogger: Any = None) -> tuple[list[dict[str, typing.Any]], typing.Any, dict[typing.Any, typing.Any], list[dict[str, typing.Any]], list[dict[str, set[str]]], collections.defaultdict[str, list[set[str]]], dict[str, typing.Any]]:", "funcdef": "def"}, "outrank.core_selftest": {"fullname": "outrank.core_selftest", "modulename": "outrank.core_selftest", "kind": "module", "doc": "

\n"}, "outrank.core_utils": {"fullname": "outrank.core_utils", "modulename": "outrank.core_utils", "kind": "module", "doc": "

\n"}, "outrank.core_utils.pro_tips": {"fullname": "outrank.core_utils.pro_tips", "modulename": "outrank.core_utils", "qualname": "pro_tips", "kind": "variable", "doc": "

\n", "default_value": "['OutRank can construct subfeatures; features based on subspaces. Example command argument is: --subfeature_mapping "feature_a->feature_b;feature_c<->feature_d;feature_c<->feature_e"', 'Heuristic MI-numba-randomized seems like the best of both worlds! (speed + performance).', 'Heuristic surrogate-lr performs cross-validation (internally), keep that in mind!', 'Consider running OutRank on a smaller data sample first, might be enough (--subsampling = a lot).', 'There are two types of combinations supported; unsupervised pairwise ranking (redundancies- --target_ranking_only=False), and supervised combinations - (--interaction_order > 1)', 'Visualization part also includes clustering - this might be very insightful!', 'By default OutRank includes feature cardinality and coverage in feature names (card; cov)', 'Intermediary checkpoints (tmp_checkpoint.tsv) might already give you insights during longer runs.', 'In theory, you can rank redundancies of combined features (--interaction_order AND --target_ranking_only=False).', 'Give it as many threads as physically possible (--num_threads).', 'You can speed up ranking by diminishing feature buffer size (--combination_number_upper_bound determines how many ranking computations per batch will be considered). This, and --subsampling are very powerful together.', 'Want to rank feature transformations, but not sure which ones to choose? --transformers=default should serve as a solid baseline (common DS transformations included).', 'Your target can be any feature! (explaining one feature with others)', 'OutRank uses HyperLogLog for cardinality estimation - this is also a potential usecase (understanding cardinalities across different data sets).', 'Each feature is named as featureName(cardinality, coverage in percents) in the final files.', 'You can generate candidate feature transformation ranges (fw) by using --task=feature_summary_transformers.']"}, "outrank.core_utils.write_json_dump_to_file": {"fullname": "outrank.core_utils.write_json_dump_to_file", "modulename": "outrank.core_utils", "qualname": "write_json_dump_to_file", "kind": "function", "doc": "

\n", "signature": "(args: Any, config_name: str) -> None:", "funcdef": "def"}, "outrank.core_utils.internal_hash": {"fullname": "outrank.core_utils.internal_hash", "modulename": "outrank.core_utils", "qualname": "internal_hash", "kind": "function", "doc": "

A generic internal hash used throughout ranking procedure - let's hardcode seed here for sure

\n", "signature": "(input_obj: str) -> str:", "funcdef": "def"}, "outrank.core_utils.DatasetInformationStorage": {"fullname": "outrank.core_utils.DatasetInformationStorage", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage", "kind": "class", "doc": "

A generic class for holding properties of a given type of dataset

\n"}, "outrank.core_utils.DatasetInformationStorage.__init__": {"fullname": "outrank.core_utils.DatasetInformationStorage.__init__", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.__init__", "kind": "function", "doc": "

\n", "signature": "(\tdata_path: str,\tcolumn_names: list[str],\tcolumn_types: set[str],\tcol_delimiter: str | None,\tencoding: str,\tfw_map: dict[str, str] | None)"}, "outrank.core_utils.DatasetInformationStorage.data_path": {"fullname": "outrank.core_utils.DatasetInformationStorage.data_path", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.data_path", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.column_names": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_names", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_names", "kind": "variable", "doc": "

\n", "annotation": ": list[str]"}, "outrank.core_utils.DatasetInformationStorage.column_types": {"fullname": "outrank.core_utils.DatasetInformationStorage.column_types", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.column_types", "kind": "variable", "doc": "

\n", "annotation": ": set[str]"}, "outrank.core_utils.DatasetInformationStorage.col_delimiter": {"fullname": "outrank.core_utils.DatasetInformationStorage.col_delimiter", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.col_delimiter", "kind": "variable", "doc": "

\n", "annotation": ": str | None"}, "outrank.core_utils.DatasetInformationStorage.encoding": {"fullname": "outrank.core_utils.DatasetInformationStorage.encoding", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.encoding", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.DatasetInformationStorage.fw_map": {"fullname": "outrank.core_utils.DatasetInformationStorage.fw_map", "modulename": "outrank.core_utils", "qualname": "DatasetInformationStorage.fw_map", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, str] | None"}, "outrank.core_utils.NumericFeatureSummary": {"fullname": "outrank.core_utils.NumericFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NumericFeatureSummary.__init__": {"fullname": "outrank.core_utils.NumericFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\tfeature_name: str,\tminimum: float,\tmaximum: float,\tmedian: float,\tnum_unique: int)"}, "outrank.core_utils.NumericFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NumericFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NumericFeatureSummary.minimum": {"fullname": "outrank.core_utils.NumericFeatureSummary.minimum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.minimum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.maximum": {"fullname": "outrank.core_utils.NumericFeatureSummary.maximum", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.maximum", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.median": {"fullname": "outrank.core_utils.NumericFeatureSummary.median", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.median", "kind": "variable", "doc": "

\n", "annotation": ": float"}, "outrank.core_utils.NumericFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NumericFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NumericFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.NominalFeatureSummary": {"fullname": "outrank.core_utils.NominalFeatureSummary", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary", "kind": "class", "doc": "

A generic class storing numeric feature statistics

\n"}, "outrank.core_utils.NominalFeatureSummary.__init__": {"fullname": "outrank.core_utils.NominalFeatureSummary.__init__", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(feature_name: str, num_unique: int)"}, "outrank.core_utils.NominalFeatureSummary.feature_name": {"fullname": "outrank.core_utils.NominalFeatureSummary.feature_name", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.feature_name", "kind": "variable", "doc": "

\n", "annotation": ": str"}, "outrank.core_utils.NominalFeatureSummary.num_unique": {"fullname": "outrank.core_utils.NominalFeatureSummary.num_unique", "modulename": "outrank.core_utils", "qualname": "NominalFeatureSummary.num_unique", "kind": "variable", "doc": "

\n", "annotation": ": int"}, "outrank.core_utils.BatchRankingSummary": {"fullname": "outrank.core_utils.BatchRankingSummary", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary", "kind": "class", "doc": "

A generic class representing batched ranking results

\n"}, "outrank.core_utils.BatchRankingSummary.__init__": {"fullname": "outrank.core_utils.BatchRankingSummary.__init__", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.__init__", "kind": "function", "doc": "

\n", "signature": "(\ttriplet_scores: list[tuple[str, str, float]],\tstep_times: dict[str, typing.Any])"}, "outrank.core_utils.BatchRankingSummary.triplet_scores": {"fullname": "outrank.core_utils.BatchRankingSummary.triplet_scores", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.triplet_scores", "kind": "variable", "doc": "

\n", "annotation": ": list[tuple[str, str, float]]"}, "outrank.core_utils.BatchRankingSummary.step_times": {"fullname": "outrank.core_utils.BatchRankingSummary.step_times", "modulename": "outrank.core_utils", "qualname": "BatchRankingSummary.step_times", "kind": "variable", "doc": "

\n", "annotation": ": dict[str, typing.Any]"}, "outrank.core_utils.display_random_tip": {"fullname": "outrank.core_utils.display_random_tip", "modulename": "outrank.core_utils", "qualname": "display_random_tip", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.get_dataset_info": {"fullname": "outrank.core_utils.get_dataset_info", "modulename": "outrank.core_utils", "qualname": "get_dataset_info", "kind": "function", "doc": "

\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.core_utils.display_tool_name": {"fullname": "outrank.core_utils.display_tool_name", "modulename": "outrank.core_utils", "qualname": "display_tool_name", "kind": "function", "doc": "

\n", "signature": "() -> None:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line": {"fullname": "outrank.core_utils.parse_ob_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_line", "kind": "function", "doc": "

Outbrain line parsing - generic TSVs

\n", "signature": "(line_string: str, delimiter: str = '\\t', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_line_vw": {"fullname": "outrank.core_utils.parse_ob_line_vw", "modulename": "outrank.core_utils", "qualname": "parse_ob_line_vw", "kind": "function", "doc": "

Parse a sparse vw line into a pandas df with pre-defined namespace

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping=None,\ttable_header=None,\tinclude_namespace_info=False) -> list[str | None]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_csv_line": {"fullname": "outrank.core_utils.parse_ob_csv_line", "modulename": "outrank.core_utils", "qualname": "parse_ob_csv_line", "kind": "function", "doc": "

Data can have commas within JSON field dumps

\n", "signature": "(line_string: str, delimiter: str = ',', args: Any = None) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.generic_line_parser": {"fullname": "outrank.core_utils.generic_line_parser", "modulename": "outrank.core_utils", "qualname": "generic_line_parser", "kind": "function", "doc": "

A generic method aimed to parse data from different sources.

\n", "signature": "(\tline_string: str,\tdelimiter: str,\targs: Any = None,\tfw_col_mapping: Any = None,\ttable_header: Any = None) -> list[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.read_reference_json": {"fullname": "outrank.core_utils.read_reference_json", "modulename": "outrank.core_utils", "qualname": "read_reference_json", "kind": "function", "doc": "

A helper method for reading a JSON

\n", "signature": "(json_path) -> dict[str, dict]:", "funcdef": "def"}, "outrank.core_utils.parse_namespace": {"fullname": "outrank.core_utils.parse_namespace", "modulename": "outrank.core_utils", "qualname": "parse_namespace", "kind": "function", "doc": "

Parse the feature namespace for type awareness

\n", "signature": "(namespace_path: str) -> tuple[set[str], dict[str, str]]:", "funcdef": "def"}, "outrank.core_utils.read_column_names": {"fullname": "outrank.core_utils.read_column_names", "modulename": "outrank.core_utils", "qualname": "read_column_names", "kind": "function", "doc": "

Read the col. header

\n", "signature": "(mapping_file: str) -> list[str]:", "funcdef": "def"}, "outrank.core_utils.parse_ob_vw_feature_information": {"fullname": "outrank.core_utils.parse_ob_vw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_vw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_raw_feature_information": {"fullname": "outrank.core_utils.parse_ob_raw_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_raw_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_ob_feature_information": {"fullname": "outrank.core_utils.parse_ob_feature_information", "modulename": "outrank.core_utils", "qualname": "parse_ob_feature_information", "kind": "function", "doc": "

A generic parser of ob-based data

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_with_description_information": {"fullname": "outrank.core_utils.parse_csv_with_description_information", "modulename": "outrank.core_utils", "qualname": "parse_csv_with_description_information", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.parse_csv_raw": {"fullname": "outrank.core_utils.parse_csv_raw", "modulename": "outrank.core_utils", "qualname": "parse_csv_raw", "kind": "function", "doc": "

\n", "signature": "(data_path) -> outrank.core_utils.DatasetInformationStorage:", "funcdef": "def"}, "outrank.core_utils.extract_features_from_reference_JSON": {"fullname": "outrank.core_utils.extract_features_from_reference_JSON", "modulename": "outrank.core_utils", "qualname": "extract_features_from_reference_JSON", "kind": "function", "doc": "

Given a model's JSON, extract unique features

\n", "signature": "(json_path: str) -> set[typing.Any]:", "funcdef": "def"}, "outrank.core_utils.summarize_feature_bounds_for_transformers": {"fullname": "outrank.core_utils.summarize_feature_bounds_for_transformers", "modulename": "outrank.core_utils", "qualname": "summarize_feature_bounds_for_transformers", "kind": "function", "doc": "

summarization auxilliary method for generating JSON-based specs

\n", "signature": "(\tbounds_object_storage: Any,\tfeature_types: list[str],\ttask_name: str,\tlabel_name: str,\tgranularity: int = 15,\toutput_summary_table_only: bool = False):", "funcdef": "def"}, "outrank.core_utils.summarize_rare_counts": {"fullname": "outrank.core_utils.summarize_rare_counts", "modulename": "outrank.core_utils", "qualname": "summarize_rare_counts", "kind": "function", "doc": "

Write rare values

\n", "signature": "(\tterm_counter: Any,\targs: Any,\tcardinality_object: Any,\tobject_info: outrank.core_utils.DatasetInformationStorage) -> None:", "funcdef": "def"}, "outrank.feature_transformations": {"fullname": "outrank.feature_transformations", "modulename": "outrank.feature_transformations", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault": {"fullname": "outrank.feature_transformations.feature_transformer_vault", "modulename": "outrank.feature_transformations.feature_transformer_vault", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.MINIMAL_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "MINIMAL_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)'}"}, "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.default_transformers.DEFAULT_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.default_transformers", "qualname": "DEFAULT_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)'}"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.FW_TRANSFORMERS", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "FW_TRANSFORMERS", "kind": "variable", "doc": "

\n", "default_value": "{'_tr_sqrt': 'np.sqrt(X)', '_tr_log(x+1)': 'np.log(X + 1)', '_tr_sqrt(abs(x))': 'np.sqrt(np.abs(X))', '_tr_log(abs(x)+1)': 'np.log(np.abs(X) + 1)', '_tr_div(x,abs(x))*log(abs(x))': 'np.divide(X, np.abs(X)) * np.log(np.abs(X))', '_tr_log(x + sqrt(pow(x,2), 1)': 'np.log(X + np.sqrt(np.power(X, 2) + 1))', '_tr_log*sqrt': 'np.log(X + 1) * np.sqrt(X)', '_tr_log*100': 'np.round(np.log(X + 1) * 100, 0)', '_tr_nonzero': 'np.where(X != 0, 1, 0)', '_tr_round(div(x,max))': 'np.round(np.divide(X, np.max(X)), 0)', '_tr_fw_sqrt_res_1_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*1,0), 0))', '_tr_fw_log_res_1_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*1,0), 0))', '_tr_fw_log_res_1_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*1,0), 0))', '_tr_fw_log_res_1_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*1,0), 0))', '_tr_fw_log_res_1_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*1,0), 0))', '_tr_fw_log_res_1_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*1,0), 0))', '_tr_fw_log_res_1_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*1,0), 0))', '_tr_fw_log_res_1_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*1,0), 0))', '_tr_fw_sqrt_res_1_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*1,0), 0))', '_tr_fw_log_res_1_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*1,0), 0))', '_tr_fw_sqrt_res_10_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*10,0), 0))', '_tr_fw_log_res_10_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*10,0), 0))', '_tr_fw_log_res_10_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*10,0), 0))', '_tr_fw_log_res_10_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*10,0), 0))', '_tr_fw_log_res_10_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*10,0), 0))', '_tr_fw_log_res_10_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*10,0), 0))', '_tr_fw_log_res_10_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*10,0), 0))', '_tr_fw_log_res_10_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*10,0), 0))', '_tr_fw_sqrt_res_10_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*10,0), 0))', '_tr_fw_log_res_10_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*10,0), 0))', '_tr_fw_sqrt_res_50_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*50,0), 0))', '_tr_fw_log_res_50_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*50,0), 0))', '_tr_fw_log_res_50_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*50,0), 0))', '_tr_fw_log_res_50_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*50,0), 0))', '_tr_fw_log_res_50_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*50,0), 0))', '_tr_fw_log_res_50_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*50,0), 0))', '_tr_fw_log_res_50_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*50,0), 0))', '_tr_fw_log_res_50_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*50,0), 0))', '_tr_fw_sqrt_res_50_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*50,0), 0))', '_tr_fw_log_res_50_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*50,0), 0))', '_tr_fw_sqrt_res_100_gt_1': 'np.where(X < 1, X, np.where(X>1 ,np.round(np.sqrt(X-1)*100,0), 0))', '_tr_fw_log_res_100_gt_1': 'np.where(X <1, X, np.where(X >1, np.round(np.log(X-1)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_2': 'np.where(X < 2, X, np.where(X>2 ,np.round(np.sqrt(X-2)*100,0), 0))', '_tr_fw_log_res_100_gt_2': 'np.where(X <2, X, np.where(X >2, np.round(np.log(X-2)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_4': 'np.where(X < 4, X, np.where(X>4 ,np.round(np.sqrt(X-4)*100,0), 0))', '_tr_fw_log_res_100_gt_4': 'np.where(X <4, X, np.where(X >4, np.round(np.log(X-4)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_8': 'np.where(X < 8, X, np.where(X>8 ,np.round(np.sqrt(X-8)*100,0), 0))', '_tr_fw_log_res_100_gt_8': 'np.where(X <8, X, np.where(X >8, np.round(np.log(X-8)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_16': 'np.where(X < 16, X, np.where(X>16 ,np.round(np.sqrt(X-16)*100,0), 0))', '_tr_fw_log_res_100_gt_16': 'np.where(X <16, X, np.where(X >16, np.round(np.log(X-16)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_32': 'np.where(X < 32, X, np.where(X>32 ,np.round(np.sqrt(X-32)*100,0), 0))', '_tr_fw_log_res_100_gt_32': 'np.where(X <32, X, np.where(X >32, np.round(np.log(X-32)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_64': 'np.where(X < 64, X, np.where(X>64 ,np.round(np.sqrt(X-64)*100,0), 0))', '_tr_fw_log_res_100_gt_64': 'np.where(X <64, X, np.where(X >64, np.round(np.log(X-64)*100,0), 0))', '_tr_fw_sqrt_res_100_gt_96': 'np.where(X < 96, X, np.where(X>96 ,np.round(np.sqrt(X-96)*100,0), 0))', '_tr_fw_log_res_100_gt_96': 'np.where(X <96, X, np.where(X >96, np.round(np.log(X-96)*100,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*1,0), 0))', '_tr_fw_prob_sqrt_res_1_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*1,0), 0))', '_tr_fw_prob_log_res_1_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*1,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*10,0), 0))', '_tr_fw_prob_sqrt_res_10_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*10,0), 0))', '_tr_fw_prob_log_res_10_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*10,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*50,0), 0))', '_tr_fw_prob_sqrt_res_50_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*50,0), 0))', '_tr_fw_prob_log_res_50_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*50,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.01': 'np.where(X < 0.01, X, np.where(X>0.01, np.round(np.sqrt(X-0.01)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.01': 'np.where(X <0.01,X, np.where(X>0.01, np.round(np.log(X-0.01)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.02': 'np.where(X < 0.02, X, np.where(X>0.02, np.round(np.sqrt(X-0.02)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.02': 'np.where(X <0.02,X, np.where(X>0.02, np.round(np.log(X-0.02)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.04': 'np.where(X < 0.04, X, np.where(X>0.04, np.round(np.sqrt(X-0.04)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.04': 'np.where(X <0.04,X, np.where(X>0.04, np.round(np.log(X-0.04)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.08': 'np.where(X < 0.08, X, np.where(X>0.08, np.round(np.sqrt(X-0.08)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.08': 'np.where(X <0.08,X, np.where(X>0.08, np.round(np.log(X-0.08)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.16': 'np.where(X < 0.16, X, np.where(X>0.16, np.round(np.sqrt(X-0.16)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.16': 'np.where(X <0.16,X, np.where(X>0.16, np.round(np.log(X-0.16)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.32': 'np.where(X < 0.32, X, np.where(X>0.32, np.round(np.sqrt(X-0.32)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.32': 'np.where(X <0.32,X, np.where(X>0.32, np.round(np.log(X-0.32)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.64': 'np.where(X < 0.64, X, np.where(X>0.64, np.round(np.sqrt(X-0.64)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.64': 'np.where(X <0.64,X, np.where(X>0.64, np.round(np.log(X-0.64)*100,0), 0))', '_tr_fw_prob_sqrt_res_100_gt_0.96': 'np.where(X < 0.96, X, np.where(X>0.96, np.round(np.sqrt(X-0.96)*100,0), 0))', '_tr_fw_prob_log_res_100_gt_0.96': 'np.where(X <0.96,X, np.where(X>0.96, np.round(np.log(X-0.96)*100,0), 0))'}"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.resolution_range": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.resolution_range", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "resolution_range", "kind": "variable", "doc": "

\n", "default_value": "[1, 10, 50, 100]"}, "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range": {"fullname": "outrank.feature_transformations.feature_transformer_vault.fw_transformers.greater_than_range", "modulename": "outrank.feature_transformations.feature_transformer_vault.fw_transformers", "qualname": "greater_than_range", "kind": "variable", "doc": "

\n", "default_value": "[1, 2, 4, 8, 16, 32, 64, 96]"}, "outrank.feature_transformations.ranking_transformers": {"fullname": "outrank.feature_transformations.ranking_transformers", "modulename": "outrank.feature_transformations.ranking_transformers", "kind": "module", "doc": "

\n"}, "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise": {"fullname": "outrank.feature_transformations.ranking_transformers.FeatureTransformerNoise", "modulename": "outrank.feature_transformations.ranking_transformers", "qualname": "FeatureTransformerNoise", "kind": "class", "doc": "

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Generate a few standard noise distributions

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Core method for generating data sets

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\n", "signature": "(args: Any):", "funcdef": "def"}, "outrank.task_selftest": {"fullname": "outrank.task_selftest", "modulename": "outrank.task_selftest", "kind": "module", "doc": "

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\n", "signature": "(args):", "funcdef": "def"}, "outrank.task_visualization": {"fullname": "outrank.task_visualization", "modulename": "outrank.task_visualization", "kind": "module", "doc": "

\n"}, "outrank.task_visualization.outrank_task_visualize_results": {"fullname": "outrank.task_visualization.outrank_task_visualize_results", "modulename": "outrank.task_visualization", "qualname": "outrank_task_visualize_results", "kind": "function", "doc": "

\n", "signature": "(args):", "funcdef": "def"}, "outrank.visualizations": {"fullname": "outrank.visualizations", "modulename": "outrank.visualizations", "kind": "module", "doc": "

\n"}, "outrank.visualizations.ranking_visualization": {"fullname": "outrank.visualizations.ranking_visualization", "modulename": "outrank.visualizations.ranking_visualization", "kind": "module", "doc": "

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A method for visualization of hierarchical clusters w.r.t. different linkage functions

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A method for visualization of the obtained feature interaction maps.

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