Basic_Models.py

"""
This script will run a manually selected training-validation split against various classification models from the scikit-learn library
Add column headers, fix missing values, and make sure class labels are in numeric form, before running models
Make sure there is only one class column, and the remaining columns are attributes you'd like to run in the model
"""


# import models
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

# import metrics/utilities
from sklearn import metrics
import pandas as pd

# clear warnings
import warnings


def warn(*args, **kwargs):
    pass


warnings.warn = warn


# =====================================================
class BasicModels:
    def __init__(self, training_name, validation_name, class_column_name):
        self.training_name = training_name
        self.validation_name = validation_name
        self.class_column_name = class_column_name

        self.x_train = None
        self.y_train = None
        self.x_val = None
        self.y_val = None

    # csv to data and labels
    def get_data_and_labels(self):
        # read the training and validation files
        training_split = pd.read_csv(self.training_name)
        validation_split = pd.read_csv(self.validation_name)

        # labels
        self.y_train = training_split[self.class_column_name]
        self.y_val = validation_split[self.class_column_name]

        training_split.drop([self.class_column_name], axis=1, inplace=True)
        validation_split.drop([self.class_column_name], axis=1, inplace=True)

        # data
        attributes = training_split.columns.values.tolist()
        print(attributes)
        self.x_train = training_split[attributes]
        self.x_val = validation_split[attributes]

    # =====================================================

    # run through tests
    def run_tests(self):
        # results dataframe
        results = pd.DataFrame(columns=['Model', 'ACC'])

        # run through models you select, easy to add more
        models = ['DT', 'SGD', 'NB', 'SVM', 'KNN', 'LR', 'MLP', 'RF']
        for model in models:
            if model == 'DT':
                clf = DecisionTreeClassifier()
            elif model == 'SGD':
                clf = SGDClassifier()
            elif model == 'RF':
                clf = RandomForestClassifier()
            elif model == 'NB':
                clf = GaussianNB()
            elif model == 'SVM':
                clf = SVC()
            elif model == 'KNN':
                clf = KNeighborsClassifier()
            elif model == 'LR':
                clf = LogisticRegression()
            elif model == 'MLP':
                clf = MLPClassifier()
            elif model == 'LDA':
                clf = LinearDiscriminantAnalysis()
            else:
                clf = None

            # run model
            clf = clf.fit(self.x_train, self.y_train)

            # predictions
            y_pred = clf.predict(self.x_val)

            # accuracy
            _acc = '{:.2f}'.format(metrics.accuracy_score(self.y_val, y_pred))
            print(model + ' accuracy: ' + str(_acc))

            # add result
            results.loc[(len(results.index))] = [model, _acc]

        return results


# =====================================================


if __name__ == '__main__':
    # input filename
    training_name = 'LUPI_WDBC_NOT_SV.csv'
    validation_name = 'LUPI_WDBC_SV.csv'
    class_column_name = 'class'

    # output filename
    output_csv_name = 'CWU_RESULTS.csv'

    m = BasicModels(training_name, validation_name, class_column_name)

    # run models
    m.get_data_and_labels()
    results = m.run_tests()

    # output results
    results.to_csv(output_csv_name, index=False)