from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.multioutput import MultiOutputClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.metrics import classification_report , accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
import pandas as pd


# In[3]:


class Pipeline_Scorer:


    
    def __init__ (self, parameters_grid,classifier):
        '''
        Initiazalizes the class.
        Takes the parameters_grid for the Cross_Validation and the classifier that the MultiOutputClassifier
        will use.

        Parameters:
        parameters_grid : dict,
            parameters for the Cross_validation
        classifier: func,
            function for the MultiOutputClassifier
        '''

        self.parameters_grid = parameters_grid
        self.classifier = classifier

    def pipeline(self, X_train,y_train,X_test,y_test,jobs,
                 scoring ='f1_micro',transformer = TfidfTransformer()):

        '''
        Make a pipeline using the splited data (train, test) with Feature Union on Count_vectorizer and the transformer selected by the user
        for the text pipeline (with TfidfTransformer() as default). For the classifier it uses
        the MultiOutputClassifier with the transformer selected.

        Takes the parameters_grid for the Cross_Validation and the classifier that the MultiOutputClassifier
        will use.

        Parameters:
        X_train: array-like of shape (n_samples, n_features),
            The train data to fit. Can be for example a list, or an array.
        y_train: array-like of shape (n_samples,) or (n_samples, n_outputs), default=None),
            The train data to fit. Can be for example a list, or an array.
        X_test: array-like of shape (n_samples, n_features),
            The test data to fit. Can be for example a list, or an array.
        y_test: array-like of shape (n_samples,) or (n_samples, n_outputs), default=None),
            The test data to fit. Can be for example a list, or an array.
        jobs: int, default=None,
            Number of jobs to run in parallel.
            Training the estimator and computing the score are parallelized over the cross-validation splits.
            None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.
        score=func, default = f1_score(),
            The evaluation metrics used for the whole model.
        scoring = callable, list, tuple, or dict, default='f1_micro'str,
            Strategy to evaluate the performance of the cross-validated model on the test set.
            For more information see the sklearn.model_selection.cross_validate documentation
        transformer = func, default = TfidfTransformer(),
            The function used inside the text pipeline for vectorizing the text data.

        Returns a fitted cross valided and prints its best parameters.
        '''

        pipeline = Pipeline([
            ('features', FeatureUnion([

                ('text_pipeline', Pipeline([
                    ('vect', CountVectorizer()),
                    ('transformer', transformer)
                ]))
            ])),
            ('clf', MultiOutputClassifier(self.classifier))
        ])

        cv = GridSearchCV(pipeline, param_grid=self.parameters_grid, scoring=scoring, n_jobs=jobs)
        cv.fit(X_train, y_train)
        self.y_pred = cv.predict(X_test)
        self.y_test = y_test
        print(cv.best_params_)


    def report(self, average = 'weighted',score=f1_score):

        '''
        Uses the fitted model in the pipeline function to make a report with the scores of each output
        of the MultiOutputClassifier and for the whole dataset.

        Parameters:
        average = str or None, default=’weighted',
            The average parameter for the score function selected.
            
        score = func,
            The metrics to use

        Returns a printed dataframe for each variable, a dataframe for the raw mean of the variables
        and a print statement for the score and average selected by the user.
        '''



        report = {}
        for n, col in enumerate(self.y_test.columns):
            output = classification_report(self.y_test[col], self.y_pred[:,n], output_dict=True)
            report[col] = {}
            for i in output:
                if i == 'accuracy':
                    break
                report[col]['f1_' + i] = output[i]['f1-score']
                report[col]['precision_' + i] = output[i]['precision']
                report[col]['recall_' + i] = output[i]['recall']

        report_df = pd.DataFrame(report).transpose()
        report_df = report_df[report_df.columns.sort_values()]
        report_df_mean = report_df.mean()

        print("Table for each column:")
        print (report_df)
        print('\n')
        print('mean of results:')
        print(report_df_mean)
        print('\n')
        print('f1-score ({}): {}'.format(average, score(self.y_test, self.y_pred, average=average)))
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.multioutput import MultiOutputClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score
from sklearn.metrics import classification_report , accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
import pandas as pd


# In[3]:


class Pipeline_Scorer:


    
    def __init__ (self, parameters_grid,classifier):
        '''
        Initiazalizes the class.
        Takes the parameters_grid for the Cross_Validation and the classifier that the MultiOutputClassifier
        will use.

        Parameters:
        parameters_grid : dict,
            parameters for the Cross_validation
        classifier: func,
            function for the MultiOutputClassifier
        '''

        self.parameters_grid = parameters_grid
        self.classifier = classifier

    def pipeline(self, X_train,y_train,X_test,y_test,jobs,
                 scoring ='f1_micro',transformer = TfidfTransformer()):

        '''
        Make a pipeline using the splited data (train, test) with Feature Union on Count_vectorizer and the transformer selected by the user
        for the text pipeline (with TfidfTransformer() as default). For the classifier it uses
        the MultiOutputClassifier with the transformer selected.

        Takes the parameters_grid for the Cross_Validation and the classifier that the MultiOutputClassifier
        will use.

        Parameters:
        X_train: array-like of shape (n_samples, n_features),
            The train data to fit. Can be for example a list, or an array.
        y_train: array-like of shape (n_samples,) or (n_samples, n_outputs), default=None),
            The train data to fit. Can be for example a list, or an array.
        X_test: array-like of shape (n_samples, n_features),
            The test data to fit. Can be for example a list, or an array.
        y_test: array-like of shape (n_samples,) or (n_samples, n_outputs), default=None),
            The test data to fit. Can be for example a list, or an array.
        jobs: int, default=None,
            Number of jobs to run in parallel.
            Training the estimator and computing the score are parallelized over the cross-validation splits.
            None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.
        score=func, default = f1_score(),
            The evaluation metrics used for the whole model.
        scoring = callable, list, tuple, or dict, default='f1_micro'str,
            Strategy to evaluate the performance of the cross-validated model on the test set.
            For more information see the sklearn.model_selection.cross_validate documentation
        transformer = func, default = TfidfTransformer(),
            The function used inside the text pipeline for vectorizing the text data.

        Returns a fitted cross valided and prints its best parameters.
        '''

        pipeline = Pipeline([
            ('features', FeatureUnion([

                ('text_pipeline', Pipeline([
                    ('vect', CountVectorizer()),
                    ('transformer', transformer)
                ]))
            ])),
            ('clf', MultiOutputClassifier(self.classifier))
        ])

        cv = GridSearchCV(pipeline, param_grid=self.parameters_grid, scoring=scoring, n_jobs=jobs)
        cv.fit(X_train, y_train)
        self.y_pred = cv.predict(X_test)
        self.y_test = y_test
        print(cv.best_params_)


    def report(self, average = 'weighted',score=f1_score):

        '''
        Uses the fitted model in the pipeline function to make a report with the scores of each output
        of the MultiOutputClassifier and for the whole dataset.

        Parameters:
        average = str or None, default=’weighted',
            The average parameter for the score function selected.
            
        score = func,
            The metrics to use

        Returns a printed dataframe for each variable, a dataframe for the raw mean of the variables
        and a print statement for the score and average selected by the user.
        '''



        report = {}
        for n, col in enumerate(self.y_test.columns):
            output = classification_report(self.y_test[col], self.y_pred[:,n], output_dict=True)
            report[col] = {}
            for i in output:
                if i == 'accuracy':
                    break
                report[col]['f1_' + i] = output[i]['f1-score']
                report[col]['precision_' + i] = output[i]['precision']
                report[col]['recall_' + i] = output[i]['recall']

        report_df = pd.DataFrame(report).transpose()
        report_df = report_df[report_df.columns.sort_values()]
        report_df_mean = report_df.mean()

        print("Table for each column:")
        print (report_df)
        print('\n')
        print('mean of results:')
        print(report_df_mean)
        print('\n')
        print('f1-score ({}): {}'.format(average, score(self.y_test, self.y_pred, average=average)))
# In[3]: Initiazalizes the class.         Takes the parameters_grid for the Cross_Validation and the classifier that the MultiOutputClassifier         will use.          Parameters:         parameters_grid : dict,             parameters for the Cross_validation         classifier: func,             function for the MultiOutputClassifier Make a pipeline using the splited data (train, test) with Feature Union on Count_vectorizer and the transformer selected by the user         for the text pipeline (with TfidfTransformer() as default). For the classifier it uses         the MultiOutputClassifier with the transformer selected.          Takes the parameters_grid for the Cross_Validation and the classifier that the MultiOutputClassifier         will use.          Parameters:         X_train: array-like of shape (n_samples, n_features),             The train data to fit. Can be for example a list, or an array.         y_train: array-like of shape (n_samples,) or (n_samples, n_outputs), default=None),             The train data to fit. Can be for example a list, or an array.         X_test: array-like of shape (n_samples, n_features),             The test data to fit. Can be for example a list, or an array.         y_test: array-like of shape (n_samples,) or (n_samples, n_outputs), default=None),             The test data to fit. Can be for example a list, or an array.         jobs: int, default=None,             Number of jobs to run in parallel.             Training the estimator and computing the score are parallelized over the cross-validation splits.             None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.         score=func, default = f1_score(),             The evaluation metrics used for the whole model.         scoring = callable, list, tuple, or dict, default='f1_micro'str,             Strategy to evaluate the performance of the cross-validated model on the test set.             For more information see the sklearn.model_selection.cross_validate documentation         transformer = func, default = TfidfTransformer(),             The function used inside the text pipeline for vectorizing the text data.          Returns a fitted cross valided and prints its best parameters. Uses the fitted model in the pipeline function to make a report with the scores of each output         of the MultiOutputClassifier and for the whole dataset.          Parameters:         average = str or None, default=’weighted',             The average parameter for the score function selected.                      score = func,             The metrics to use          Returns a printed dataframe for each variable, a dataframe for the raw mean of the variables         and a print statement for the score and average selected by the user.