cdplib/cdplib/db_migration/MigrationCleaning.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 08:09:52 2019

@author: tanya
"""

import os
import sys
import pandas as pd
import numpy as np
import gc

sys.path.append(os.getcwd())

from cdplib.db_migration.ParseMapping import ParseMapping
from cdplib.db_migration.ParseJsonSchema import ParseJsonSchema
from cdplib.utils.ExceptionsHandler import ExceptionsHandler
from cdplib.utils.CleaningUtils import CleaningUtils
from cdplib.log import Log
import json
from boltons.iterutils import remap

class MigrationCleaning:
    '''
    Class for correcting and filtering the incorrect data.
    We keep the correcting and the filtering methods separated,
    since there might be other custom steps in between.
    '''
    def __init__(self, mapping_paths: (str, list),
                 schema_paths: (str, list),
                 inconsist_report_table: str = None,
                 filter_index_columns: (str, list) = None,
                 mapping_source: str = "internal_name",
                 mapping_target: str = "mongo_name",
                 mapping_parser: type = ParseMapping,
                 schema_parser: type = ParseJsonSchema):
        '''
        '''
        self.log = Log('Migration Cleaning')
        self._exception_handler = ExceptionsHandler()

        assert isinstance(inconsist_report_table, str),\
            "Inconsistent report table should be a tablename string"

        self._inconsist_report_table = inconsist_report_table

        assert isinstance(filter_index_columns, (str, list)),\
            "Filter index columns must be a str or a list"

        self._filter_index_columns = list(filter_index_columns)

        self._schema_parser = schema_parser(schema_paths)

        self._mapping_parser = mapping_parser(mapping_paths,
                                              source=mapping_source,
                                              target=mapping_target)

        self._mapping_paths = mapping_paths
        self._schema_paths = schema_paths

        from cdplib.db_handlers.SQLHandler import SQLHandler
        self._sql_db = SQLHandler()

    def _assert_dataframe_input(self, data: pd.DataFrame):
        '''
        '''
        assert(isinstance(data, pd.DataFrame)),\
            "Parameter 'data' must be a pandas dataframe"

    @property
    def _field_mapping(self):
        '''
        '''
        return self._mapping_parser.get_field_mapping()

    @property
    def _required_fields(self):
        '''
        '''
        source_required_fields = self._mapping_parser.get_required_fields()
        target_required_fields = self._schema_parser.get_required_fields()

        for source_field, target_field in self._field_mapping.items():

            if (target_field in target_required_fields) and\
                    (source_field not in source_required_fields):

                source_required_fields.append(source_field)

        return source_required_fields

    @property
    def _default_values(self):
        '''
        Returns a dictonary in which the default values of the mongo schema
        are mapped to the default values of the migration mapping. In migration
        mapping the default values should be specified as the values which
        doesn't contain any information and can be seen therefore as an empty
        value. 
        '''
        default_values = {}

        target_default_values = self._schema_parser.get_default_values()
        source_default_values = self._mapping_parser.get_default_values()

        for source_field, target_field in self._field_mapping.items():

            if source_field not in source_default_values:
                continue

            elif target_field not in target_default_values:
                target_default_values[target_field] = np.nan

            default_values[source_field] = {
                    target_default_values[target_field]:
                    source_default_values[source_field]
                    }
  
        return default_values

    @property
    def _python_types(self):
        '''
        '''
        target_types = self._schema_parser.get_python_types()
        result = {}

        for source_field, target_field in self._field_mapping.items():

            if target_field in target_types:
                result[source_field] = target_types[target_field]

            """
            date_type_mismatch =\
                    (target_field in target_types) and\
                    (source_field in source_types) and\
                    (target_types[target_field] == str) and\
                    (source_types[source_field] == np.dtype('<M8[ns]'))

            if date_type_mismatch:
                target_types[target_field] = np.dtype('<M8[ns]')

            if (source_field in source_types) and\
                    (target_field in target_types) and\
                    (target_types[target_field] != source_types[source_field]):

                self.log.log_and_raise_error(("Type {0} of field {1} "
                                    "in schema does not match "
                                    "type {2} of field {3} in "
                                    "migration mapping")
                                   .format(target_types[target_field],
                                           target_field,
                                           source_types[source_field],
                                           source_field))

            if target_field in target_types:
                source_types[source_field] = target_types[target_field]

            """

        return result

    @property
    def _value_mappings(self):
        '''
        '''
        return self._mapping_parser.get_value_mappings()

    @property
    def _date_formats(self):
        '''
        '''
        return self._mapping_parser.get_date_formats()

    def _get_mongo_schema_info(self, method_name: str):
        '''
        '''
        result = {}

        target_dict = getattr(self._schema_parser, method_name)()

        for source_field, target_field in self._field_mapping.items():

            if target_field in target_dict:

                result[source_field] = target_dict[target_field]

        return result

    @property
    def _allowed_values(self):
        '''
        '''
        return self._get_mongo_schema_info("get_allowed_values")

    @property
    def _minimum_values(self):
        '''
        '''
        return self._get_mongo_schema_info("get_minimum_value")

    @property
    def _maximum_values(self):
        '''
        '''
        return self._get_mongo_schema_info("get_maximum_value")

    @property
    def _patterns(self):
        '''
        '''
        return self._get_mongo_schema_info("get_patterns")

    def _filter_invalid_data(self, data: pd.DataFrame,
                             invalid_mask: pd.Series,
                             reason: (str, pd.Series)) -> pd.DataFrame:
        '''
        '''
        assert((self._inconsist_report_table is not None) and
               (self._filter_index_columns is not None)),\
            "Inconsistent report table or filter index is not provided"

        self._assert_dataframe_input(data)

        data = data.copy(deep=True)

        db = self._sql_db

        if invalid_mask.sum() == 0:
            return data

        data_inconsist = data.assign(reason=reason)\
                             .loc[invalid_mask]\
                             .reset_index(drop=True)
                                  
        if db.check_if_table_exists(self._inconsist_report_table):
            columns = db.get_column_names(tablename=self._inconsist_report_table)

            if len(columns) > 0:
                columns_not_in_data = [column for column in columns if column not in data.columns]
                for value in columns_not_in_data:
                    data_inconsist[value] = 'Column does not exist in the mongo database and has therefore been dropped'
                data_inconsist = data_inconsist[columns]

        db.append_to_table(data=data_inconsist,
                           tablename=self._inconsist_report_table)

        n_rows_filtered = len(data_inconsist)
        n_instances_filtered = len(data_inconsist[self._filter_index_columns].drop_duplicates())

        del data_inconsist
        gc.collect()

        self.log.warning(("Filtering: {0} ."
                           "Filtered {1} rows "
                           "and {2} instances"
                           .format(reason, n_rows_filtered, n_instances_filtered)))

        nok_index_data = data.loc[invalid_mask, self._filter_index_columns]\
                             .drop_duplicates().reset_index(drop=True)

        nok_index = pd.MultiIndex.from_arrays([nok_index_data[c] for c in
                                               self._filter_index_columns])

        all_index = pd.MultiIndex.from_arrays([data[c] for c in
                                               self._filter_index_columns])

        data = data.loc[~all_index.isin(nok_index)].reset_index(drop=True)

        return data

    def _replace_values(self, data: pd.DataFrame,
                        default: bool) -> pd.DataFrame:
        '''
        '''
        if default:
            default_str = "default"
        else:
            default_str = "equal"

        self._assert_dataframe_input(data)

        data = data.copy(deep=True)

        if default:
            mapping = self._default_values
        else:
            mapping = self._value_mappings

        for column, d in mapping.items():

            try:

                if column not in data.columns:
                    continue

                dtype = data[column].dtype

                for key, values in d.items():

                    if not default:
                    
                        mask = (data[column].astype(str).isin(values))

                    else:

                        mask = (data[column].isin(values))

                    if default:

                        mask = mask | (data[column].isnull())

                    data.loc[mask, column] = key
                    
                data[column] = data[column].astype(dtype)

            except Exception as e:

               self.log.log_and_raise_error(("Failed to replace {0} values "
                                    "in {1}. Exit with error {2}"
                                    .format(default_str, column, e)))

        self.log.info("Replaced {} values".format(default_str))

        return data

    def replace_default_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        return self._replace_values(data=data, default=True)

    def map_equal_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        return self._replace_values(data=data, default=False)

    def convert_types(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        self._assert_dataframe_input(data)

        for column, python_type in self._python_types.items():

            try:
                if column not in data.columns:
                    continue

                elif column in self._date_formats:

                    data[column] = CleaningUtils.convert_dates(
                            series=data[column],
                            formats=self._date_formats[column])

                elif (python_type == int) and data[column].isnull().any():

                    self.log.log_and_raise_error(("Column {} contains missing values "
                                        "and cannot be of integer type"
                                        .format(column)))

                elif python_type == bool:

                    accepted_bool = {'ja': True, 'j': True, '1': True, 1: True,
                                     'yes': True, 'y': True, 'true':True,
                                     't': True, 'nein': False, 'n': False,
                                     'no': False, 'false': False, 'f': False,
                                     '0': False, 0: False}
                    data[column] = data[column].map(accepted_bool)
                    data[column] = data[column].astype(bool)


                elif python_type == str:

                    # might not be the smoothes solution but it works
                    python_type = str
                    data = data.copy(deep=True)
                    data[column] = data[column].astype(python_type)
                    python_type = object
                    data[column] = data[column].astype(python_type)
                    
                elif python_type == float:
                    data = data.fillna(np.inf)
                    data[column] = data[column].astype(python_type)

                else:

                    data = data.copy(deep=True)
                    data[column] = data[column].astype(python_type)

                if data[column].dtype != python_type:

                    self.log.warning(("After conversion type in {0} "
                                       "should be {1} "
                                       "but is still {2}"
                                       .format(column,
                                               python_type,
                                               data[column].dtype)))

            except Exception as e:

                self.log.log_and_raise_error(("Failed to convert types in {0}. "
                                    "Exit with error {1}"
                                    .format(column, e)))

        self.log.info("Converted dtypes")

        return data

    def filter_invalid_missing_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        self._assert_dataframe_input(data)

        for column in data.columns:

            if (column in self._required_fields) and\
                    (data[column].isnull().any()):

                invalid_mask = data[column].isnull()

                reason = "Null value in the required field {}"\
                         .format(column)

                data = self._filter_invalid_data(data=data,
                                                 invalid_mask=invalid_mask,
                                                 reason=reason)

        return data

    def filter_invalid_types(self, data: pd.DataFrame) -> pd.DataFrame():
        '''
        '''
        self._assert_dataframe_input(data)

        for column in data.columns:

            if column not in self._python_types:
                continue

            python_type = self._python_types[column]
            
            #Needs to be done since coumn dtype of strings is a object
            if python_type == str:
                python_type = object
                       
            if data[column].dtype != python_type:

                def mismatch_type(x):
                    return type(x) != python_type

                invalid_mask = data[column].apply(mismatch_type)

                reason = "Type mismatch in field {}".format(column)

                data = self._filter_invalid_data(data=data,
                                                 invalid_mask=invalid_mask,
                                                 reason=reason)

        return data

    def filter_invalid_patterns(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        self._assert_dataframe_input(data)

        for column in data.columns:

            if column not in self._patterns:
                continue

            pattern = self._patterns[column]

            invalid_mask = (~data[column].astype(str).str.match(pattern))

            reason = "Pattern mismatch in field {0}. Pattern: {1}Example: {2}"\
                    .format(column,pattern,data.iloc[0][column])

            data = self._filter_invalid_data(data=data,
                                             invalid_mask=invalid_mask,
                                             reason=reason)
        return data

    def filter_invalid_values(self, data: pd.DataFrame) -> pd.DataFrame:
        '''
        '''
        for column in data.columns:

            if column in self._minimum_values:

                min_value = self._minimum_values[column]

                invalid_mask = data[column] > min_value

                reason = "Too large values in field {}".format(column)

                data = self._filter_invalid_data(data=data,
                                                 invalid_mask=invalid_mask,
                                                 reason=reason)

            elif column in self._maximum_values:

                max_value = self._maximum_values[column]

                invalid_mask = data[column] < max_value

                reason = "Too small values in field {}".format(column)

                data = self._filter_invalid_data(data=data,
                                                 invalid_mask=invalid_mask,
                                                 reason=reason)

            elif column in self._allowed_values:
                
                allowed_values = self._allowed_values[column]

                invalid_mask = (~data[column].isin(allowed_values))

                not_allowed_examples = data.loc[invalid_mask, column].unique()[:3]

                reason = "Not allowed values {0}... in field {1}"\
                         .format(not_allowed_examples, column)

                data = self._filter_invalid_data(data=data,
                                                 invalid_mask=invalid_mask,
                                                 reason=reason)

            else:
                continue

        return data
    
    def drop_columns_with_no_content(self, data: pd.DataFrame) -> pd.DataFrame():
        '''
        '''
        data = data.dropna(how ='all', axis='columns')
        for column in data.columns: 
            unique_values = data[column].unique() 
            no_content_signs = [None, '-', 'n.a'] 
            intersection = list(set(unique_values) & set(no_content_signs))
            if len(intersection) - len(unique_values) == 0:
                data = data.drop(columns=[column])
            
        return data
    
    def clean_json_from_None_object(self, data: pd.DataFrame) -> pd.DataFrame():
        data = data.to_json(date_format="iso")
        data = json.loads(data)
        new_data = remap(data, lambda p, k, v: v is not None)
        new_data = remap(new_data, lambda p, k, v: v != 'None')
        new_data = remap(new_data, lambda p, k, v: v != 'inf')
        new_data = remap(new_data, lambda p, k, v: (isinstance(v,bool) or (not isinstance(v,bool) and bool(v))))
        return new_data

    def restrict_to_collection(self, data: pd.DataFrame, collection_name: str) -> pd.DataFrame:
        '''
        '''
        mongo_fields = self._schema_parser.get_fields_restricted_to_collection(collection_name=collection_name)
        
        mapping_fields = self._mapping_parser.get_fields_restricted_to_collection(collection_name=collection_name)
          
        return data[[c for c in data.columns if (c in mapping_fields) or (c in mongo_fields)]]


if __name__ == "__main__":

    # testing

    from cdplib.db_handlers.SQLHandler import SQLHandler

    mapping_path = os.path.join(".", "migration_mappings", "rs1_mapping.json")

    schema_paths = [
            os.path.join(".", "mongo_schema", "schema_wheelsets.json"),
            os.path.join(".", "mongo_schema", "schema_process_instances.json")]

    inconsist_report_table = "test_inconsist_report_rs1"

    if all([os.path.isfile(p) for p in schema_paths + [mapping_path]]):

        cleaner = MigrationCleaning(
                mapping_paths=mapping_path,
                schema_paths=schema_paths,
                mapping_source="internal_name",
                mapping_target="mongo_name",
                filter_index_columns=["radsatznummer"],
                inconsist_report_table=inconsist_report_table)

        db = SQLHandler()

        data = db.read_sql_to_dataframe("select * from rs1 limit 100")

        data = cleaner.replace_default_values(data)

        data = cleaner.map_equal_values(data)

        data = cleaner.convert_types(data)

        non_filtered_len = len(data)

        data = cleaner.filter_invalid_types(data)

        if len(data) < non_filtered_len:

            data = cleaner.convert_types(data)

        data = cleaner.filter_invalid_null_values(data)

        data = cleaner.filter_invalid_patterns(data)

        data = cleaner.filter_notallowed_values(data)

    print("Done!")