import pandas as pd
from pkg_resources.py2_warn import msg
from sklearn.linear_model import LogisticRegression
from typing import Text,Dict

from sklearn.metrics import f1_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC

import logging
import os


logging_str="[% (asctime)s: %(levelname)s: %(module)s] %(module)s"
log_dir="logs"
os.makedirs(log_dir,exist_ok=True)
logging.basicConfig(filename=os.path.join(log_dir,"running_logs.log",level=logging.INFO,format=logging_str))

class UnsupportedClassifier(Exception):
    def __init__(self,estimator_name):
        self.msg=f'Unsupported estimator{estimator_name}'
        super().__init__(self,msg)


def get_supported_estimator()-> Dict:
    """
    Returns:
        Dict: supported classifiers
    """
    logging.info("Let Create the supported estimator:")
    return{
        'logreg':LogisticRegression,
        'svm':SVC,
        'knn':KNeighborsClassifier
    }

def train(df:pd.DataFrame,target_column:Text,
          estimator_name:Text,param_grid:Dict,cv:int):
    """ Train model.'''

    Args:
        df{pandas.DataFrame}:dataset
        target_column{Text}:target column name
        estimator_name{Text}:estimator_name
        param_grid{Dict}:grid parameters
        CV{int}:cross_validation value

    Returns:
        trained model
    """
    logging.info("Let start the process:")
    estimators = get_supported_estimator()

    if estimator_name not in estimators.keys():
        raise UnsupportedClassifier(estimator_name)

    estimator = estimators[estimator_name]()
    f1_scorer= make_scorer(f1_score,average='weighted')
    clf = GridSearchCV(estimator=estimator,
                       param_grid=param_grid,
                       cv=cv,
                       verbose=1,
                       scoring=f1_scorer)

    #Get X and Y


    y_train=df.loc[: ,target_column].values.astype("int32")
    X_train=df.drop(target_column,axis=1).values.astype('float32')

    clf.train(X_train,y_train)

    return clf





def train_lr(df: pd.DataFrame, target_column: Text) -> LogisticRegression:
    
    
    
    logging.info("Let start the process:")

    # Get X and Y
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')

    # Create an instance of Logistic Regression Classifier CV and fit the data

    logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class='multinomial', max_iter=100)
    logreg.fit(X_train, y_train)

    return logreg

9e4ebc6b26bae9e7d875038b7acb4d20e6ebfd99
<mask token>


class UnsupportedClassifier(Exception):

    def __init__(self, estimator_name):
        self.msg = f'Unsupported estimator{estimator_name}'
        super().__init__(self, msg)


def get_supported_estimator() ->Dict:
    """
    Returns:
        Dict: supported classifiers
    """
    logging.info('Let Create the supported estimator:')
    return {'logreg': LogisticRegression, 'svm': SVC, 'knn':
        KNeighborsClassifier}


def train(df: pd.DataFrame, target_column: Text, estimator_name: Text,
    param_grid: Dict, cv: int):
    """ Train model.'''

    Args:
        df{pandas.DataFrame}:dataset
        target_column{Text}:target column name
        estimator_name{Text}:estimator_name
        param_grid{Dict}:grid parameters
        CV{int}:cross_validation value

    Returns:
        trained model
    """
    logging.info('Let start the process:')
    estimators = get_supported_estimator()
    if estimator_name not in estimators.keys():
        raise UnsupportedClassifier(estimator_name)
    estimator = estimators[estimator_name]()
    f1_scorer = make_scorer(f1_score, average='weighted')
    clf = GridSearchCV(estimator=estimator, param_grid=param_grid, cv=cv,
        verbose=1, scoring=f1_scorer)
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    clf.train(X_train, y_train)
    return clf


def train_lr(df: pd.DataFrame, target_column: Text) ->LogisticRegression:
    logging.info('Let start the process:')
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class=
        'multinomial', max_iter=100)
    logreg.fit(X_train, y_train)
    return logreg

<mask token>
os.makedirs(log_dir, exist_ok=True)
logging.basicConfig(filename=os.path.join(log_dir, 'running_logs.log',
    level=logging.INFO, format=logging_str))


class UnsupportedClassifier(Exception):

    def __init__(self, estimator_name):
        self.msg = f'Unsupported estimator{estimator_name}'
        super().__init__(self, msg)


def get_supported_estimator() ->Dict:
    """
    Returns:
        Dict: supported classifiers
    """
    logging.info('Let Create the supported estimator:')
    return {'logreg': LogisticRegression, 'svm': SVC, 'knn':
        KNeighborsClassifier}


def train(df: pd.DataFrame, target_column: Text, estimator_name: Text,
    param_grid: Dict, cv: int):
    """ Train model.'''

    Args:
        df{pandas.DataFrame}:dataset
        target_column{Text}:target column name
        estimator_name{Text}:estimator_name
        param_grid{Dict}:grid parameters
        CV{int}:cross_validation value

    Returns:
        trained model
    """
    logging.info('Let start the process:')
    estimators = get_supported_estimator()
    if estimator_name not in estimators.keys():
        raise UnsupportedClassifier(estimator_name)
    estimator = estimators[estimator_name]()
    f1_scorer = make_scorer(f1_score, average='weighted')
    clf = GridSearchCV(estimator=estimator, param_grid=param_grid, cv=cv,
        verbose=1, scoring=f1_scorer)
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    clf.train(X_train, y_train)
    return clf


def train_lr(df: pd.DataFrame, target_column: Text) ->LogisticRegression:
    logging.info('Let start the process:')
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class=
        'multinomial', max_iter=100)
    logreg.fit(X_train, y_train)
    return logreg

<mask token>
logging_str = '[% (asctime)s: %(levelname)s: %(module)s] %(module)s'
log_dir = 'logs'
os.makedirs(log_dir, exist_ok=True)
logging.basicConfig(filename=os.path.join(log_dir, 'running_logs.log',
    level=logging.INFO, format=logging_str))


class UnsupportedClassifier(Exception):

    def __init__(self, estimator_name):
        self.msg = f'Unsupported estimator{estimator_name}'
        super().__init__(self, msg)


def get_supported_estimator() ->Dict:
    """
    Returns:
        Dict: supported classifiers
    """
    logging.info('Let Create the supported estimator:')
    return {'logreg': LogisticRegression, 'svm': SVC, 'knn':
        KNeighborsClassifier}


def train(df: pd.DataFrame, target_column: Text, estimator_name: Text,
    param_grid: Dict, cv: int):
    """ Train model.'''

    Args:
        df{pandas.DataFrame}:dataset
        target_column{Text}:target column name
        estimator_name{Text}:estimator_name
        param_grid{Dict}:grid parameters
        CV{int}:cross_validation value

    Returns:
        trained model
    """
    logging.info('Let start the process:')
    estimators = get_supported_estimator()
    if estimator_name not in estimators.keys():
        raise UnsupportedClassifier(estimator_name)
    estimator = estimators[estimator_name]()
    f1_scorer = make_scorer(f1_score, average='weighted')
    clf = GridSearchCV(estimator=estimator, param_grid=param_grid, cv=cv,
        verbose=1, scoring=f1_scorer)
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    clf.train(X_train, y_train)
    return clf


def train_lr(df: pd.DataFrame, target_column: Text) ->LogisticRegression:
    logging.info('Let start the process:')
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class=
        'multinomial', max_iter=100)
    logreg.fit(X_train, y_train)
    return logreg

import pandas as pd
from pkg_resources.py2_warn import msg
from sklearn.linear_model import LogisticRegression
from typing import Text, Dict
from sklearn.metrics import f1_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
import logging
import os
logging_str = '[% (asctime)s: %(levelname)s: %(module)s] %(module)s'
log_dir = 'logs'
os.makedirs(log_dir, exist_ok=True)
logging.basicConfig(filename=os.path.join(log_dir, 'running_logs.log',
    level=logging.INFO, format=logging_str))


class UnsupportedClassifier(Exception):

    def __init__(self, estimator_name):
        self.msg = f'Unsupported estimator{estimator_name}'
        super().__init__(self, msg)


def get_supported_estimator() ->Dict:
    """
    Returns:
        Dict: supported classifiers
    """
    logging.info('Let Create the supported estimator:')
    return {'logreg': LogisticRegression, 'svm': SVC, 'knn':
        KNeighborsClassifier}


def train(df: pd.DataFrame, target_column: Text, estimator_name: Text,
    param_grid: Dict, cv: int):
    """ Train model.'''

    Args:
        df{pandas.DataFrame}:dataset
        target_column{Text}:target column name
        estimator_name{Text}:estimator_name
        param_grid{Dict}:grid parameters
        CV{int}:cross_validation value

    Returns:
        trained model
    """
    logging.info('Let start the process:')
    estimators = get_supported_estimator()
    if estimator_name not in estimators.keys():
        raise UnsupportedClassifier(estimator_name)
    estimator = estimators[estimator_name]()
    f1_scorer = make_scorer(f1_score, average='weighted')
    clf = GridSearchCV(estimator=estimator, param_grid=param_grid, cv=cv,
        verbose=1, scoring=f1_scorer)
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    clf.train(X_train, y_train)
    return clf


def train_lr(df: pd.DataFrame, target_column: Text) ->LogisticRegression:
    logging.info('Let start the process:')
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')
    logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class=
        'multinomial', max_iter=100)
    logreg.fit(X_train, y_train)
    return logreg

import pandas as pd
from pkg_resources.py2_warn import msg
from sklearn.linear_model import LogisticRegression
from typing import Text,Dict

from sklearn.metrics import f1_score, make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC

import logging
import os


logging_str="[% (asctime)s: %(levelname)s: %(module)s] %(module)s"
log_dir="logs"
os.makedirs(log_dir,exist_ok=True)
logging.basicConfig(filename=os.path.join(log_dir,"running_logs.log",level=logging.INFO,format=logging_str))

class UnsupportedClassifier(Exception):
    def __init__(self,estimator_name):
        self.msg=f'Unsupported estimator{estimator_name}'
        super().__init__(self,msg)


def get_supported_estimator()-> Dict:
    """
    Returns:
        Dict: supported classifiers
    """
    logging.info("Let Create the supported estimator:")
    return{
        'logreg':LogisticRegression,
        'svm':SVC,
        'knn':KNeighborsClassifier
    }

def train(df:pd.DataFrame,target_column:Text,
          estimator_name:Text,param_grid:Dict,cv:int):
    """ Train model.'''

    Args:
        df{pandas.DataFrame}:dataset
        target_column{Text}:target column name
        estimator_name{Text}:estimator_name
        param_grid{Dict}:grid parameters
        CV{int}:cross_validation value

    Returns:
        trained model
    """
    logging.info("Let start the process:")
    estimators = get_supported_estimator()

    if estimator_name not in estimators.keys():
        raise UnsupportedClassifier(estimator_name)

    estimator = estimators[estimator_name]()
    f1_scorer= make_scorer(f1_score,average='weighted')
    clf = GridSearchCV(estimator=estimator,
                       param_grid=param_grid,
                       cv=cv,
                       verbose=1,
                       scoring=f1_scorer)

    #Get X and Y


    y_train=df.loc[: ,target_column].values.astype("int32")
    X_train=df.drop(target_column,axis=1).values.astype('float32')

    clf.train(X_train,y_train)

    return clf





def train_lr(df: pd.DataFrame, target_column: Text) -> LogisticRegression:
    
    
    
    logging.info("Let start the process:")

    # Get X and Y
    y_train = df.loc[:, target_column].values.astype('int32')
    X_train = df.drop(target_column, axis=1).values.astype('float32')

    # Create an instance of Logistic Regression Classifier CV and fit the data

    logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class='multinomial', max_iter=100)
    logreg.fit(X_train, y_train)

    return logreg
