def get_value_meta(self, value, meta_conf): # type: (pd.DataFrame, ValueMetaConf) -> ValueMeta data_schema = {} if meta_conf.log_schema: data_schema.update({ "type": self.type_str, "columns": list(value.columns), "shape": value.shape, "dtypes": {col: str(type_) for col, type_ in value.dtypes.items()}, }) if meta_conf.log_size: data_schema["size"] = int(value.size) if meta_conf.log_preview: value_preview = self.to_preview( value, preview_size=meta_conf.get_preview_size()) data_hash = fast_hasher.hash( hash_pandas_object(value, index=True).values) else: value_preview = None data_hash = None return ValueMeta( value_preview=value_preview, data_dimensions=value.shape, data_schema=data_schema, data_hash=data_hash, )
def test_df_value_meta(self, pandas_data_frame): expected_data_schema = { "type": DataFrameValueType.type_str, "columns": list(pandas_data_frame.columns), "size": int(pandas_data_frame.size), "shape": pandas_data_frame.shape, "dtypes": { col: str(type_) for col, type_ in pandas_data_frame.dtypes.items() }, } meta_conf = ValueMetaConf.enabled() expected_value_meta = ValueMeta( value_preview=DataFrameValueType().to_preview( pandas_data_frame, preview_size=meta_conf.get_preview_size()), data_dimensions=pandas_data_frame.shape, data_schema=expected_data_schema, data_hash=fast_hasher.hash( hash_pandas_object(pandas_data_frame, index=True).values), ) df_value_meta = DataFrameValueType().get_value_meta( pandas_data_frame, meta_conf=meta_conf) assert df_value_meta.value_preview == expected_value_meta.value_preview assert df_value_meta.data_hash == expected_value_meta.data_hash assert json_utils.dumps(df_value_meta.data_schema) == json_utils.dumps( expected_value_meta.data_schema) assert df_value_meta.data_dimensions == expected_value_meta.data_dimensions assert df_value_meta == expected_value_meta
def test_str_value_meta(self): str_value_meta = StrValueType().get_value_meta("foo", ValueMetaConf.enabled()) expected_value_meta = ValueMeta( value_preview="foo", data_dimensions=None, data_schema={"type": "str"}, data_hash=fast_hasher.hash("foo"), ) assert str_value_meta == expected_value_meta
def test_df_value_meta( self, pandas_data_frame, pandas_data_frame_histograms, pandas_data_frame_stats ): expected_data_schema = { "type": DataFrameValueType.type_str, "columns": list(pandas_data_frame.columns), "size": int(pandas_data_frame.size), "shape": pandas_data_frame.shape, "dtypes": { col: str(type_) for col, type_ in pandas_data_frame.dtypes.items() }, } meta_conf = ValueMetaConf.enabled() expected_value_meta = ValueMeta( value_preview=DataFrameValueType().to_preview( pandas_data_frame, preview_size=meta_conf.get_preview_size() ), data_dimensions=pandas_data_frame.shape, data_schema=expected_data_schema, data_hash=fast_hasher.hash( hash_pandas_object(pandas_data_frame, index=True).values ), descriptive_stats=pandas_data_frame_stats, histograms=pandas_data_frame_histograms, ) df_value_meta = DataFrameValueType().get_value_meta( pandas_data_frame, meta_conf=meta_conf ) assert df_value_meta.value_preview == expected_value_meta.value_preview assert df_value_meta.data_hash == expected_value_meta.data_hash assert json_utils.dumps(df_value_meta.data_schema) == json_utils.dumps( expected_value_meta.data_schema ) assert df_value_meta.data_dimensions == expected_value_meta.data_dimensions std = df_value_meta.descriptive_stats["Births"].pop("std") expected_std = expected_value_meta.descriptive_stats["Births"].pop("std") assert round(std, 2) == expected_std df_value_meta.descriptive_stats["Names"].pop("top") assert df_value_meta.descriptive_stats == expected_value_meta.descriptive_stats counts, values = df_value_meta.histograms.pop("Names") expected_counts, expected_values = expected_value_meta.histograms.pop("Names") assert counts == expected_counts assert set(values) == set(expected_values) # order changes in each run # histograms are tested in histogram tests and they change a lot, no need to test also here df_value_meta.histograms = expected_value_meta.histograms = None expected_value_meta.histogram_system_metrics = ( df_value_meta.histogram_system_metrics ) assert df_value_meta.data_schema == expected_value_meta.data_schema assert attr.asdict(df_value_meta) == attr.asdict(expected_value_meta)
def test_target_value_meta(self): v = target("a") meta_conf = ValueMetaConf.enabled() target_value_meta = TargetPathLibValueType().get_value_meta( v, meta_conf=meta_conf) expected_value_meta = ValueMeta( value_preview='"a"', data_dimensions=None, data_schema={"type": "Path"}, data_hash=fast_hasher.hash(v), ) assert target_value_meta == expected_value_meta
def get_value_meta(self, value, meta_conf): # type: (pd.DataFrame, ValueMetaConf) -> ValueMeta data_schema = {} if meta_conf.log_schema: data_schema.update({ "type": self.type_str, "columns": list(value.columns), "shape": value.shape, "dtypes": {col: str(type_) for col, type_ in value.dtypes.items()}, }) if meta_conf.log_size: data_schema["size.bytes"] = int(value.size) value_preview, data_hash = None, None if meta_conf.log_preview: value_preview = self.to_preview( value, preview_size=meta_conf.get_preview_size()) try: data_hash = fast_hasher.hash( hash_pandas_object(value, index=True).values) except Exception as e: logger.warning( "Could not hash dataframe object %s! Exception: %s", value, e) if meta_conf.log_histograms: start_time = time.time() stats, histograms = PandasHistograms( value, meta_conf).get_histograms_and_stats() hist_sys_metrics = { "histograms_and_stats_calc_time": time.time() - start_time } else: stats, histograms = {}, {} hist_sys_metrics = None return ValueMeta( value_preview=value_preview, data_dimensions=value.shape, data_schema=data_schema, data_hash=data_hash, descriptive_stats=stats, histogram_system_metrics=hist_sys_metrics, histograms=histograms, )
def test_df_value_meta(self, pandas_data_frame): expected_data_schema = { "type": DataFrameValueType.type_str, "columns": list(pandas_data_frame.columns), "size.bytes": int(pandas_data_frame.size), "shape": pandas_data_frame.shape, "dtypes": { col: str(type_) for col, type_ in pandas_data_frame.dtypes.items() }, } meta_conf = ValueMetaConf.enabled() expected_value_meta = ValueMeta( value_preview=DataFrameValueType().to_preview( pandas_data_frame, preview_size=meta_conf.get_preview_size()), data_dimensions=pandas_data_frame.shape, data_schema=expected_data_schema, data_hash=fast_hasher.hash( hash_pandas_object(pandas_data_frame, index=True).values), ) df_value_meta = DataFrameValueType().get_value_meta( pandas_data_frame, meta_conf=meta_conf) assert df_value_meta.value_preview == expected_value_meta.value_preview assert df_value_meta.data_hash == expected_value_meta.data_hash assert df_value_meta.data_schema == expected_value_meta.data_schema assert df_value_meta.data_dimensions == expected_value_meta.data_dimensions assert df_value_meta.data_schema == expected_value_meta.data_schema # histograms and stats are tested in histogram tests and they change a lot, no need to test also here assert set([ col_stats.column_name for col_stats in df_value_meta.columns_stats ]) == {"Names", "Births"} assert set(df_value_meta.histograms.keys()) == {"Names", "Births"}
def _safe_hash(value): try: return fast_hasher.hash(value) except: logger.info("Failed to hash value of type %s", type(value)) return None
def to_signature(self, x): shape = "[%s]" % (",".join(map(str, x.shape))) return "%s:%s" % (shape, fast_hasher.hash(x))
def to_signature(self, x): return fast_hasher.hash(x)
def get_data_hash(self, value): return fast_hasher.hash(value)
def get_data_hash(self, value): return fast_hasher.hash(hash_pandas_object(value, index=True).values)