#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
#

"""
Credit Risk Models

The module contains model definitions of various tested models for credit
assessment
"""

from typing import Any, Dict, List
import joblib
import logging
import pandas as pd
from django.core.exceptions import BadRequest
from sklearn.preprocessing import LabelEncoder

log = logging.getLogger(__name__)


class Classifier(object):
    """
    Basic Scorecard Model

    Warning: This class should not be used directly. Use derived classes
    instead.
    """
    def __init__(self,
                 model=None,
                 categorical=[],
                 label_encoders: List[LabelEncoder] = {}):

        self.model = model
        self.categorical = categorical
        self.label_encoders = label_encoders

    # def __str__(self):
    #     return f"""
    #     Model Object
    #     ----------------------------------------------------------------

    #     Classifier: {self.classifier().__class__.__name__}
    #     Test Size: {self.test_size}
    #     Random State: {self.random_state}
    #     Number of Splits: {self.n_splits}
    #     Parameter Grid: {self.params}

    #     {self.model}
    #     """

    def preprocessing(self, data: Dict[str, Any]):
        """
        Preprocess python dict object for prediction

        Parameters
        ----------
        data: dict
            dictionary of data to predict
        """

        categorical = [x for x in self.categorical if x != 'risk']

        # log.info(f"Categorical: {categorical}")

        # for category in categorical:
        #     if category not in list(data.keys()):
        #         data[category] = None

        for key, value in data.items():
            if type(value) == str:
                data[key] = value

        data = pd.DataFrame(data, index=[0])

        # fill missing values
        # data.fillna(self.values_fill_missing)

        le = self.label_encoders
        data = data.dropna()

        # convert categoricals
        for category in categorical:
            failed_trials = []
            try:
                data[category] = le[category].transform(data[category])
            except KeyError as e:
                failed_trials.append(e)
                log.debug(f"An error occured: {str(e)}")
                if len(failed_trials) >= 3:
                    raise BadRequest(failed_trials)
                else:
                    data[e] = None

        return data

    def predict(self, data):
        """
        Predict scorecard model

        Args:
            data: array
                Data to perform prediction on.
        """
        return self.model.predict_proba(data)

    def postprocessing(self, prediction):
        label = "bad"
        if prediction[1] > 0.5:
            label = "good"
        return {"probability": prediction[1], "label": label}

    def compute_prediction(self, data: Dict[str, Any]):
        try:
            input_data = self.preprocessing(data)
            prediction = self.predict(input_data)[0]
            prediction = self.postprocessing(prediction)
        except Exception as e:
            log.debug(f'An error occured: {str(e)}')
            raise BadRequest(str(e))

        return prediction


class RandomForestClassifier(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/rf_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(RandomForestClassifier, self).__init__(model, categorical,
                                                     label_encoders)


class SVC(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/svc_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(SVC, self).__init__(model, categorical, label_encoders)


class MLP(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/mlp_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(MLP, self).__init__(model, categorical, label_encoders)


class GradientBoostClassifier(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/gb_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(GradientBoostClassifier, self).__init__(model, categorical,
                                                      label_encoders)

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
#

"""
Credit Risk Models

The module contains model definitions of various tested models for credit
assessment
"""

from typing import Any, Dict, List
import joblib
import logging
import pandas as pd
from django.core.exceptions import BadRequest
from sklearn.preprocessing import LabelEncoder

log = logging.getLogger(__name__)


class Classifier(object):
    """
    Basic Scorecard Model

    Warning: This class should not be used directly. Use derived classes
    instead.
    """
    def __init__(self,
                 model=None,
                 categorical=[],
                 label_encoders: List[LabelEncoder] = {}):

        self.model = model
        self.categorical = categorical
        self.label_encoders = label_encoders

    # def __str__(self):
    #     return f"""
    #     Model Object
    #     ----------------------------------------------------------------

    #     Classifier: {self.classifier().__class__.__name__}
    #     Test Size: {self.test_size}
    #     Random State: {self.random_state}
    #     Number of Splits: {self.n_splits}
    #     Parameter Grid: {self.params}

    #     {self.model}
    #     """

    def preprocessing(self, data: Dict[str, Any]):
        """
        Preprocess python dict object for prediction

        Parameters
        ----------
        data: dict
            dictionary of data to predict
        """

        categorical = [x for x in self.categorical if x != 'risk']

        # log.info(f"Categorical: {categorical}")

        # for category in categorical:
        #     if category not in list(data.keys()):
        #         data[category] = None

        for key, value in data.items():
            if type(value) == str:
                data[key] = value

        data = pd.DataFrame(data, index=[0])

        # fill missing values
        # data.fillna(self.values_fill_missing)

        le = self.label_encoders
        data = data.dropna()

        # convert categoricals
        for category in categorical:
            failed_trials = []
            try:
                data[category] = le[category].transform(data[category])
            except KeyError as e:
                failed_trials.append(e)
                log.debug(f"An error occured: {str(e)}")
                if len(failed_trials) >= 3:
                    raise BadRequest(failed_trials)
                else:
                    data[e] = None

        return data

    def predict(self, data):
        """
        Predict scorecard model

        Args:
            data: array
                Data to perform prediction on.
        """
        return self.model.predict_proba(data)

    def postprocessing(self, prediction):
        label = "bad"
        if prediction[1] > 0.5:
            label = "good"
        return {"probability": prediction[1], "label": label}

    def compute_prediction(self, data: Dict[str, Any]):
        try:
            input_data = self.preprocessing(data)
            prediction = self.predict(input_data)[0]
            prediction = self.postprocessing(prediction)
        except Exception as e:
            log.debug(f'An error occured: {str(e)}')
            raise BadRequest(str(e))

        return prediction


class RandomForestClassifier(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/rf_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(RandomForestClassifier, self).__init__(model, categorical,
                                                     label_encoders)


class SVC(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/svc_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(SVC, self).__init__(model, categorical, label_encoders)


class MLP(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/mlp_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(MLP, self).__init__(model, categorical, label_encoders)


class GradientBoostClassifier(Classifier):
    def __init__(
        self,
        model=joblib.load('zoo/models/german/gb_classifier.joblib'),
        categorical=joblib.load('zoo/models/german/categorical.joblib'),
        label_encoders=joblib.load('zoo/models/german/label_encoders.joblib')):
        super(GradientBoostClassifier, self).__init__(model, categorical,
                                                      label_encoders)

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. # Credit Risk Models
 
 The module contains model definitions of various tested models for credit
 assessment Basic Scorecard Model
 
     Warning: This class should not be used directly. Use derived classes
     instead. # def __str__(self): #     return f""" #     Model Object #     ---------------------------------------------------------------- #     Classifier: {self.classifier().__class__.__name__} #     Test Size: {self.test_size} #     Random State: {self.random_state} #     Number of Splits: {self.n_splits} #     Parameter Grid: {self.params} #     {self.model} #     """ Preprocess python dict object for prediction
 
         Parameters
         ----------
         data: dict
             dictionary of data to predict # log.info(f"Categorical: {categorical}") # for category in categorical: #     if category not in list(data.keys()): #         data[category] = None # fill missing values # data.fillna(self.values_fill_missing) # convert categoricals Predict scorecard model
 
         Args:
             data: array
                 Data to perform prediction on.