project.py

# -*- coding: utf-8 -*-
import sys, copy
import numpy as np
import lib.io
import lib.viz
import lib.cl

def main(argv):

    input_filename_x = 'train_data.csv'
    input_filename_y = 'train_labels.csv'    

    test_input_filename = 'test_data.csv'

    svc_model_filename = 'svc_classif.pkl'
    lr_model_filename = 'lr_classif.pkl'
    rfc_model_filename = 'rfc_classif.pkl'
    rfc_feat_imp_filename = 'rfc_feat_imp.png'
    model_comp_result_chart_filename = 'method_comp_res.png'
    
    io = lib.io.IO()
    viz = lib.viz.Viz()
    cl = lib.cl.CL(io, viz)
    
    # Read data
    X, y = io.read_data(input_filename_x, input_filename_y)
    test_x = io.read_data(test_input_filename, None)
    X_ = copy.deepcopy(X)
    y_ = copy.deepcopy(y)
    
    print "There are " + str(len(X)) + " samples in the train set."
    print "There are " + str(len(test_x)) + " samples in the test set."
    
    test_x = np.matrix(test_x)
    test_ids = range(1, len(test_x)+1)    

    # Remove outliers
    X, y = cl.lof(np.matrix(X), np.matrix(y))
    X_, y_ = cl.lof(np.matrix(X_), np.matrix(y_))

    # Shuffle
    X, y = io.shuffle(X, y)
    X_, y_ = io.shuffle(X_, y_)

    # PCA
    X = cl.pca(np.matrix(X), 'pca_explained_variance.png').tolist()
    test_x = cl.pca(np.matrix(test_x), None).tolist()

    # Split data to train and validation set
    # mini_batches
    # ids, batches_x, batches_y = io.split_data(X, y, 100, 100)
    
    val_ids, val_x, val_y = io.pick_set(X, y, 726)
    _, no_pca_val_x, no_pca_val_y = io.pick_set(X_, y_, 726)
    train_ids, train_x, train_y = io.pick_set(X, y, 3200)
    _, no_pca_train_x, no_pca_train_y = io.pick_set(X_, y_, 3200)
    
    # Train
    cl.svc_cl_train(train_x, train_y, filename=svc_model_filename)
    cl.lr_cl_train(train_x, train_y, filename=lr_model_filename)
    cl.rfc_cl_train(no_pca_train_x, no_pca_train_y,
                    filename=rfc_model_filename,
                    feat_imp_plot_filename=rfc_feat_imp_filename)
    # cl.svc_cl_load(svc_model_filename)
    # cl.lr_cl_load(lr_model_filename)
    # cl.rfc_cl_load(rfc_model_filename)

    # validate
    results = {}
    results['SVC'] = cl.svc_cl_val(val_x, val_y)
    results['Linear Regression']  = cl.lr_cl_val(val_x, val_y)
    results['Random Forest Classifier'] = cl.rfc_cl_val(no_pca_val_x, no_pca_val_y)

    # Draw some results
    viz.model_comp_results(results, model_comp_result_chart_filename)
    
    
    # pred_class, pred_proba = cl.svc_cl_pred(val_x)
    # pred_class, pred_proba = cl.rfc_cl_pred(val_x)    

    # predict
    pred_class, pred_proba = cl.lr_cl_pred(test_x)
    
    # Output
    io.write_classes('classes_sub_result.csv', test_ids, pred_class)
    io.write_probabilities('probabilities_sub_result.csv', test_ids, pred_proba)

if __name__ == "__main__":
    main(sys.argv[1:])