proj1_helpers.py
e1ba221822d9master

proj1_helpers.py
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			# -- coding: utf-8 --
			"""some helper functions for project 1."""
			import csv
			import numpy as np


			def load_csv_data(data_path, sub_sample=False):
			"""Loads data and returns y (class labels), tX (features) and ids (event ids)"""
			y = np.genfromtxt(data_path, delimiter=",", skip_header=1, dtype=str, usecols=1)
			x = np.genfromtxt(data_path, delimiter=",", skip_header=1)
			ids = x[:, 0].astype(np.int)
			input_data = x[:, 2:]

			# convert class labels from strings to binary (-1,1)
			yb = np.ones(len(y))
			yb[np.where(y=='b')] = -1

			# sub-sample
			if sub_sample:
			yb = yb[::50]
			input_data = input_data[::50]
			ids = ids[::50]

			return yb, input_data, ids


			def predict_labels(weights, data):
			"""Generates class predictions given weights, and a test data matrix"""
			y_pred = np.dot(data, weights)
			y_pred[np.where(y_pred <= 0)] = -1
			y_pred[np.where(y_pred > 0)] = 1

			return y_pred


			def create_csv_submission(ids, y_pred, name):
			"""
			Creates an output file in csv format for submission to kaggle
			Arguments: ids (event ids associated with each prediction)
			y_pred (predicted class labels)
			name (string name of .csv output file to be created)
			"""
			with open(name, 'w') as csvfile:
			fieldnames = ['Id', 'Prediction']
			writer = csv.DictWriter(csvfile, delimiter=",", fieldnames=fieldnames)
			writer.writeheader()
			for r1, r2 in zip(ids, y_pred):
			writer.writerow({'Id':int(r1),'Prediction':int(r2)})