def test_experiment_run_and_compare(): for data_path in datasets: accuracies = {} learner_types = ['prior_probability', 'decision_tree'] for learner_type in learner_types: accuracies[learner_type] = run(data_path, learner_type, 1.0)[1] if 'candy' in data_path or 'ivy' in data_path: assert (accuracies['decision_tree'] > accuracies['prior_probability'])
def test_experiment_run_prior_probability(): accuracies = {} for data_path in datasets: learner_type = 'prior_probability' confusion_matrix, accuracy, precision, recall, f1_measure = (run( data_path, learner_type, 1.0)) accuracies[data_path] = accuracy dataset = xp_dataset_name('ivy-league.csv') assert (accuracies[dataset] > .2)
def test_experiment_run_decision_tree(): accuracies = {} for data_path in datasets: learner_type = 'decision_tree' confusion_matrix, accuracy, precision, recall, f1_measure = (run( data_path, learner_type, 1.0)) accuracies[data_path] = accuracy dataset = [dataset for dataset in datasets if 'ivy-league.csv' in dataset][0] assert (accuracies[dataset] > .7)
def test_experiment_run_decision_tree(): accuracies = {} for data_path in datasets: learner_type = 'decision_tree' confusion_matrix, accuracy, precision, recall, f1_measure = (run( data_path, learner_type, 1.0)) accuracies[data_path] = accuracy accuracy_goals = { xp_dataset_name('ivy-league.csv'): .95, xp_dataset_name('xor.csv'): 1.0, xp_dataset_name('candy-data.csv'): .75, xp_dataset_name('majority-rule.csv'): 1.0 } for key in accuracy_goals: assert (accuracies[key] >= accuracy_goals[key])
from code import run # Adelson Jhonata Silva de Sousa – 18/0114913 # José Fortes Neto - 16/0128331 # pip install pandas # pip install sklearn # python main.py run()
def __run_code(self): # +------------- YOUR CODE HERE -------------+ import code code.run(echo=self.screen.print, **self.config) # +------------------------------------------+ self.__post_run_update()
# necessary imports import matplotlib.pyplot as plt import pandas as pd import code # read csv data = pd.read_csv('data.csv') # extract from csv x = list(data[data.columns[0]]) y = list(data[data.columns[1]]) # obtains best value of m and b for line m, b = code.run() # plot all the points of data using scatter plot plt.scatter(x, y) line = [m * i + b for i in x] # plot the linear regression line plt.plot(x, line) plt.title("Linear Regression using gradient descent") plt.plot() plt.savefig('figure.jpg')
import json from code import run config_file = open('config.json') config = json.load(config_file) config_file.close() run(config)