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It looks like some files got commited recently (in NVIDIA-Merlin#521) with windows style linefeed/carriage returns instead of just the linefeed in the rest of the codebase. Fix so we don't generate massive whitespace diffs on every commit.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,101 +1,101 @@ | ||
| # | ||
| # Copyright (c) 2020, NVIDIA CORPORATION. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
| import dask_cudf | ||
| import numpy as np | ||
| from nvtx import annotate | ||
|
|
||
| from .operator import ColumnNames | ||
| from .stat_operator import StatOperator | ||
|
|
||
|
|
||
| class DataStats(StatOperator): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.col_names = [] | ||
| self.col_types = [] | ||
| self.col_dtypes = [] | ||
| self.output = {} | ||
|
|
||
| @annotate("DataStats_fit", color="green", domain="nvt_python") | ||
| def fit(self, columns: ColumnNames, ddf: dask_cudf.DataFrame): | ||
| dask_stats = {} | ||
|
|
||
| ddf_dtypes = ddf.head(1) | ||
|
|
||
| # For each column, calculate the stats | ||
| for col in columns: | ||
| dask_stats[col] = {} | ||
| self.col_names.append(col) | ||
| # Get dtype for all | ||
| dtype = ddf_dtypes[col].dtype | ||
| self.col_dtypes.append(dtype) | ||
|
|
||
| # Identify column type | ||
| if np.issubdtype(dtype, np.floating): | ||
| col_type = "conts" | ||
| else: | ||
| col_type = "cats" | ||
| self.col_types.append(col_type) | ||
|
|
||
| # Get cardinality for cats | ||
| if col_type == "cats": | ||
| dask_stats[col]["cardinality"] = ddf[col].nunique() | ||
|
|
||
| # if string, replace string for their lengths for the rest of the computations | ||
| if dtype == "object": | ||
| ddf[col] = ddf[col].map_partitions(lambda x: x.str.len(), meta=("x", int)) | ||
| # Add list support when cudf supports it: | ||
| # https://github.com/rapidsai/cudf/issues/7157 | ||
| # elif col_type == "cat_mh": | ||
| # ddf[col] = ddf[col].map_partitions(lambda x: x.list.len()) | ||
|
|
||
| # Get min,max, and mean | ||
| dask_stats[col]["min"] = ddf[col].min() | ||
| dask_stats[col]["max"] = ddf[col].max() | ||
| dask_stats[col]["mean"] = ddf[col].mean() | ||
|
|
||
| # Get std only for conts | ||
| if col_type == "conts": | ||
| dask_stats[col]["std"] = ddf[col].std() | ||
|
|
||
| # Get Percentage of NaNs for all | ||
| dask_stats[col]["per_nan"] = 100 * (1 - ddf[col].count() / len(ddf[col])) | ||
|
|
||
| return dask_stats | ||
|
|
||
| def fit_finalize(self, dask_stats): | ||
| for i, col in enumerate(self.col_names): | ||
| # Add dtype | ||
| dask_stats[col]["dtype"] = str(self.col_dtypes[i]) | ||
| # Cast types for yaml | ||
| if isinstance(dask_stats[col]["mean"], np.floating): | ||
| dask_stats[col]["mean"] = dask_stats[col]["mean"].item() | ||
| if isinstance(dask_stats[col]["per_nan"], np.floating): | ||
| dask_stats[col]["per_nan"] = dask_stats[col]["per_nan"].item() | ||
| if self.col_types[i] == "conts": | ||
| if isinstance(dask_stats[col]["std"], np.floating): | ||
| dask_stats[col]["std"] = dask_stats[col]["std"].item() | ||
| else: | ||
| if isinstance(dask_stats[col]["cardinality"], np.integer): | ||
| dask_stats[col]["cardinality"] = dask_stats[col]["cardinality"].item() | ||
| self.output = dask_stats | ||
|
|
||
| def clear(self): | ||
| self.output = {} | ||
|
|
||
| # transform.__doc__ = Operator.transform.__doc__ | ||
| fit.__doc__ = StatOperator.fit.__doc__ | ||
| fit_finalize.__doc__ = StatOperator.fit_finalize.__doc__ | ||
| # | ||
| # Copyright (c) 2020, NVIDIA CORPORATION. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
| import dask_cudf | ||
| import numpy as np | ||
| from nvtx import annotate | ||
|
|
||
| from .operator import ColumnNames | ||
| from .stat_operator import StatOperator | ||
|
|
||
|
|
||
| class DataStats(StatOperator): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.col_names = [] | ||
| self.col_types = [] | ||
| self.col_dtypes = [] | ||
| self.output = {} | ||
|
|
||
| @annotate("DataStats_fit", color="green", domain="nvt_python") | ||
| def fit(self, columns: ColumnNames, ddf: dask_cudf.DataFrame): | ||
| dask_stats = {} | ||
|
|
||
| ddf_dtypes = ddf.head(1) | ||
|
|
||
| # For each column, calculate the stats | ||
| for col in columns: | ||
| dask_stats[col] = {} | ||
| self.col_names.append(col) | ||
| # Get dtype for all | ||
| dtype = ddf_dtypes[col].dtype | ||
| self.col_dtypes.append(dtype) | ||
|
|
||
| # Identify column type | ||
| if np.issubdtype(dtype, np.floating): | ||
| col_type = "conts" | ||
| else: | ||
| col_type = "cats" | ||
| self.col_types.append(col_type) | ||
|
|
||
| # Get cardinality for cats | ||
| if col_type == "cats": | ||
| dask_stats[col]["cardinality"] = ddf[col].nunique() | ||
|
|
||
| # if string, replace string for their lengths for the rest of the computations | ||
| if dtype == "object": | ||
| ddf[col] = ddf[col].map_partitions(lambda x: x.str.len(), meta=("x", int)) | ||
| # Add list support when cudf supports it: | ||
| # https://github.com/rapidsai/cudf/issues/7157 | ||
| # elif col_type == "cat_mh": | ||
| # ddf[col] = ddf[col].map_partitions(lambda x: x.list.len()) | ||
|
|
||
| # Get min,max, and mean | ||
| dask_stats[col]["min"] = ddf[col].min() | ||
| dask_stats[col]["max"] = ddf[col].max() | ||
| dask_stats[col]["mean"] = ddf[col].mean() | ||
|
|
||
| # Get std only for conts | ||
| if col_type == "conts": | ||
| dask_stats[col]["std"] = ddf[col].std() | ||
|
|
||
| # Get Percentage of NaNs for all | ||
| dask_stats[col]["per_nan"] = 100 * (1 - ddf[col].count() / len(ddf[col])) | ||
|
|
||
| return dask_stats | ||
|
|
||
| def fit_finalize(self, dask_stats): | ||
| for i, col in enumerate(self.col_names): | ||
| # Add dtype | ||
| dask_stats[col]["dtype"] = str(self.col_dtypes[i]) | ||
| # Cast types for yaml | ||
| if isinstance(dask_stats[col]["mean"], np.floating): | ||
| dask_stats[col]["mean"] = dask_stats[col]["mean"].item() | ||
| if isinstance(dask_stats[col]["per_nan"], np.floating): | ||
| dask_stats[col]["per_nan"] = dask_stats[col]["per_nan"].item() | ||
| if self.col_types[i] == "conts": | ||
| if isinstance(dask_stats[col]["std"], np.floating): | ||
| dask_stats[col]["std"] = dask_stats[col]["std"].item() | ||
| else: | ||
| if isinstance(dask_stats[col]["cardinality"], np.integer): | ||
| dask_stats[col]["cardinality"] = dask_stats[col]["cardinality"].item() | ||
| self.output = dask_stats | ||
|
|
||
| def clear(self): | ||
| self.output = {} | ||
|
|
||
| # transform.__doc__ = Operator.transform.__doc__ | ||
| fit.__doc__ = StatOperator.fit.__doc__ | ||
| fit_finalize.__doc__ = StatOperator.fit_finalize.__doc__ |
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