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How to use numeric_dtypes method in hypothesis

Best Python code snippet using hypothesis

preprocessor_dispatcher.py

Source:preprocessor_dispatcher.py

1from sklearn import compose, impute, pipeline, preprocessing2from src import config3NUMERIC_DTYPES = ["int64", "float64"]4def get_numeric_cols():5    if config.CONTINUOUS_FEATURES:6        return config.CONTINUOUS_FEATURES7    return compose.make_column_selector(dtype_include=NUMERIC_DTYPES)8def get_categorical_cols():9    if config.DISCRETE_FEATURES:10        return config.DISCRETE_FEATURES11    return compose.make_column_selector(dtype_exclude=NUMERIC_DTYPES)12imputers = {13    "constant": impute.SimpleImputer(strategy="constant", fill_value="unknown"),14    "knn": impute.KNNImputer(),15    "mean": impute.SimpleImputer(),16    "median": impute.SimpleImputer(strategy="median"),17    "mode": impute.SimpleImputer(strategy="most_frequent"),18}19encoders = {20    "one_hot": preprocessing.OneHotEncoder(handle_unknown="ignore"),21    "ordinal": preprocessing.OrdinalEncoder(22        handle_unknown="use_encoded_value", unknown_value=-1023    ),24}25scalers = {26    "max_abs": preprocessing.MaxAbsScaler(),27    "min_max": preprocessing.MinMaxScaler(),28    "standard": preprocessing.StandardScaler(),29}30numeric_pipelines = {31    "linear": pipeline.Pipeline(32        [("standard_scaler", scalers["standard"]), ("mean_imputer", imputers["mean"])],33        verbose=config.VERBOSITY,34    ),35    "tree": pipeline.Pipeline(36        [("mean_imputer", imputers["mean"])], verbose=config.VERBOSITY37    ),38}39categorical_pipelines = {40    "linear": pipeline.Pipeline(41        [("one_hot_encoder", encoders["one_hot"])], verbose=config.VERBOSITY42    ),43    "tree": pipeline.Pipeline(44        [("mode_imputer", imputers["mode"]), ("ordinal_encoder", encoders["ordinal"])],45        verbose=config.VERBOSITY,46    ),47}48preprocessors = {49    "linear_numeric": compose.make_column_transformer(50        (numeric_pipelines["linear"], get_numeric_cols()),51        n_jobs=-1,52        verbose=config.VERBOSE,53    ),54    "tree_numeric": compose.make_column_transformer(55        (numeric_pipelines["tree"], get_numeric_cols()),56        n_jobs=-1,57        verbose=config.VERBOSE,58    ),59    "linear_categorical": compose.make_column_transformer(60        (categorical_pipelines["linear"], get_categorical_cols()),61        n_jobs=-1,62        verbose=config.VERBOSE,63    ),64    "tree_categorical": compose.make_column_transformer(65        (categorical_pipelines["tree"], get_categorical_cols()),66        n_jobs=-1,67        verbose=config.VERBOSE,68    ),69    "linear_all": compose.make_column_transformer(70        (numeric_pipelines["linear"], get_numeric_cols()),71        (categorical_pipelines["linear"], get_categorical_cols()),72        n_jobs=-1,73        verbose=config.VERBOSE,74    ),75    "tree_all": compose.make_column_transformer(76        (numeric_pipelines["tree"], get_numeric_cols()),77        (categorical_pipelines["tree"], get_categorical_cols()),78        n_jobs=-1,79        verbose=config.VERBOSE,80    ),...

test_pretty.py

Source:test_pretty.py

...31        ("from_dtype", from_dtype(xp, xp.int8)),32        ("arrays", arrays(xp, xp.int8, 5)),33        ("scalar_dtypes", scalar_dtypes(xp)),34        ("boolean_dtypes", boolean_dtypes(xp)),35        ("numeric_dtypes", numeric_dtypes(xp)),36        ("integer_dtypes", integer_dtypes(xp)),37        ("unsigned_integer_dtypes", unsigned_integer_dtypes(xp)),38        ("floating_dtypes", floating_dtypes(xp)),39        # Namespaced strategies40        ("from_dtype", xps.from_dtype(xp.int8)),41        ("arrays", xps.arrays(xp.int8, 5)),42        ("scalar_dtypes", xps.scalar_dtypes()),43        ("boolean_dtypes", xps.boolean_dtypes()),44        ("numeric_dtypes", xps.numeric_dtypes()),45        ("integer_dtypes", xps.integer_dtypes()),46        ("unsigned_integer_dtypes", xps.unsigned_integer_dtypes()),47        ("floating_dtypes", xps.floating_dtypes()),48    ]49)50def test_xp_strategies_pretty_repr(name, strat):51    """Strategies that take xp use its __name__ for their own repr."""52    assert repr(strat).startswith(name), f"{name} not in strat repr"53    assert len(repr(strat)) < 100, "strat repr looks too long"...

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