def train_ignore_features(): data_limit = 10 model_name = 'first_linear_then_more_GraphConvs_then_linear' predict_type = 'y_combine_all_percent' train_d, train_f, val_d, val_f, word_to_ixs = data(data_limit) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Number of features', NODE_FEATURES_NUM) x_list = list(range(NODE_FEATURES_NUM)) auc_list = [] for i in x_list: my_models = MyModels(word_to_ixs, ignore_columns=[i]) net = my_models.my_models[model_name] criterion = nn.CrossEntropyLoss( weight=torch.tensor([1.0, 6.0], device=device)) training_h, validation_h, whole_training_h = run(net, train_d, val_d, device=device, criterion=criterion, batch_size=10, epochs=2, model_name=model_name, save=False) thresholds, aucs = calculate_metrics(val_d, net, print_model_name=None, do_plot=False, save=False) auc_list.append(aucs[predict_type]) plt.figure() plt.bar(x_list, auc_list) plt.show()
import main as mn import sqlite3 as sql con = sql.connect("Pyth") usr = input("Username: "******"Password: "******"insert into data values ('" + usr + "','" + x["hash"] + "','" + str(x["num"]) + "')") con.commit()
import altair as alt import main import pandas as pd source = main.data() chart = alt.Chart(source).mark_bar().encode(x='id', y="loc") # The highlight will be set on the result of a conditional statement chart_1 = alt.Chart(source).mark_circle(size=60).encode( x='id', y='CC', color='file_name', #tooltip=['Origin', 'Horsepower', 'Miles_per_Gallon'] ).interactive() chart_2 = alt.Chart(source).mark_line().encode(x='id', y='CC') chart.save('chart.html')
false_pos = [] right = [] A = 1 B = 1 C = 1 D = 1 E = 1 correct_attempts = 0 THRESHOLD = 500 with open("data.txt", "r") as f: for line in f.readlines(): pos = line.find(":") userdata = "" exec("userdata = " + line[pos + 1:-1]) todo.append([line[0:pos], main.data(userdata)]) while percentage < 70 or not false_pos: wrong = [] right = [] false_pos = [] correct_attempts = 0 for item in todo: score = user.checkTest(item[1], A, B, C, D, E) if (score >= THRESHOLD and item[0] == "joe"): correct_attempts += 1 wrong.append(item) elif (score < THRESHOLD and item[0] != "joe"): false_pos.append(item) wrong.append(item) else:
import numpy as np import main, pickle #load the data from the main script norm = main.data()[0] test_data = main.data()[3] test_labels = main.data()[4] total = main.data()[6] hot_label = main.data()[7] #load the parameters from the main script net = main.network()[0] #activation function def f(z): if z > 100.0: return 1.0 elif z < -100.0: return 0.0 else: return 1.0 / (1.0 + np.exp(-z)) x = [] #input to each neuron y = [] #activation of each neuron for layer in range(len(net)): x.append(np.array([0.0] * net[layer])) y.append(np.array([0.0] * net[layer]))
import random, sys, pickle import numpy as np import main, test #load the data from the main script norm = main.data()[0] training_data = main.data()[1] training_labels = main.data()[2] check_test = main.data()[5] hot_label = main.data()[7] #load the parameters from the main script net = main.network()[0] epochs = main.network()[1] number_examples = main.network()[2] mini_batch_size = main.network()[3] suffle_training = main.network()[4] learning_rate = main.network()[5] momentum_rate = main.network()[6] tau = main.network()[7] period = main.network()[8] #activation function def f(z): if z > 100.0: return 1.0 elif z < -100.0: return 0.0 else: return 1.0 / (1.0 + np.exp(-z))