Python find_breakeven Examples

Example #1

File: average_noise_levels.py Project: hexiangquan/CNN

        for data_path in paths:
            json_data = util.open_json_result(os.path.join(path, folder, sub['name'], data_path))
            if type(json_data) is list:
                d = json_data[0]
            else:
                d = json_data
            data[folder][sub['name']].append(d)


#Average and find breakeven points, for final series.
breakeven_points = {}
for folder in folders:
    breakeven_points[folder] = []
    for sub in sub_folders:
        pr_avg = util.average(data[folder][sub['name']], pr_key_y, pr_key_x)
        breakeven = util.find_breakeven(pr_avg, samples=4)
        breakeven_points[folder].append({"x": sub['value'], "y": breakeven[1]})
print breakeven_points
#series.append({"name": folder, "data": pr_avg})
#

#Summary figure for MSE loss
loss_points = {}
for folder in folders:
    loss_points[folder] = []
    for sub in sub_folders:
        loss_avg = util.average(data[folder][sub['name']], 'events', lc_key_x)
        last_loss = loss_avg[-1][lc_key_y]
        loss_points[folder].append({"x": sub['value'], "y": last_loss})

Example #2

File: average_compare.py Project: olavvatne/CNN

for folder in folders:
    paths = os.listdir(os.path.join(path, folder, sub_folder))
    nr_tests += len(paths)
    print("Folder", folder, "length", len(paths))
    all_tests.append(paths)
    data[folder] = []

for t in range(len(all_tests)):
    for data_path in all_tests[t]:
        json_data = util.open_json_result(os.path.join(path, folders[t], sub_folder, data_path))

        if type(json_data) is list:
            d = json_data[0]
        else:
            d = json_data
        data[folders[t]].append(d)

series = []
for folder in folders:
    pr_avg = util.average(data[folder], pr_key_y, pr_key_x)
    breakeven_points = util.find_breakeven(pr_avg, samples=30)
    print(folder, breakeven_points)
    series.append({"name": folder, "data": pr_avg, "breakeven": breakeven_points})
util.display_precision_recall_plot(series)

series = []
for folder in folders:
    loss_avg = util.average(data[folder], 'events', lc_key_x)
    print(folder, loss_avg[-1]['test_loss'])
    series.append({"name": folder, "data": loss_avg, "y_key": lc_key_y})
util.display_loss_curve_plot(series)

Example #3

File: loss_precisionrecall_compare.py Project: hexiangquan/CNN

        data[folders[t]].append(d)

series = []

manual_breakeven = [
    [0.513, 0.595, 0.687, 0.712], [0.337, 0.411, 0.462, 0.524]
]  #Very uneven curves is hard to approximate by polyfit. (finds breakeven automatically)
compare_series = []
for j, pr in enumerate(folders[1:]):
    pr_per_epoch = []
    for i, curve in enumerate(data[pr]):
        samples = 10
        if i < len(manual_breakeven[j]):
            breakeven_points = [0, manual_breakeven[j][i]]
        else:
            breakeven_points = util.find_breakeven(curve[pr_key_y],
                                                   samples=samples)
        print(folder, breakeven_points)
        name = (pr_epoch * i) + 5
        series.append({
            "name": "Epoch " + str(name),
            "data": curve[pr_key_y],
            "breakeven": breakeven_points
        })
        pr_per_epoch.append({'epoch': name, 'breakeven': breakeven_points[-1]})
    compare_series.append({
        'name': pr,
        "data": pr_per_epoch,
        'y_key': 'breakeven'
    })
util.display_precision_recall_plot(series)

Example #4

File: loss_precisionrecall_compare.py Project: olavvatne/CNN

        else:
            d = json_data
        data[folders[t]].append(d)

series = []

manual_breakeven = [
    [0.513, 0.595, 0.687, 0.712],
    [0.337, 0.411, 0.462, 0.524],
]  # Very uneven curves is hard to approximate by polyfit. (finds breakeven automatically)
compare_series = []
for j, pr in enumerate(folders[1:]):
    pr_per_epoch = []
    for i, curve in enumerate(data[pr]):
        samples = 10
        if i < len(manual_breakeven[j]):
            breakeven_points = [0, manual_breakeven[j][i]]
        else:
            breakeven_points = util.find_breakeven(curve[pr_key_y], samples=samples)
        print(folder, breakeven_points)
        name = (pr_epoch * i) + 5
        series.append({"name": "Epoch " + str(name), "data": curve[pr_key_y], "breakeven": breakeven_points})
        pr_per_epoch.append({"epoch": name, "breakeven": breakeven_points[-1]})
    compare_series.append({"name": pr, "data": pr_per_epoch, "y_key": "breakeven"})
util.display_precision_recall_plot(series)

series = []
loss_avg = util.average(data[folders[0]], "events", lc_key_x)
series.append({"name": folders[0], "data": loss_avg, "y_key": lc_key_y})
util.display_two_axis_plot(series, compare_series)

Example #5

File: average_compare.py Project: hexiangquan/CNN

    data[folder] = []

for t in range(len(all_tests)):
    for data_path in all_tests[t]:
        json_data = util.open_json_result(
            os.path.join(path, folders[t], sub_folder, data_path))

        if type(json_data) is list:
            d = json_data[0]
        else:
            d = json_data
        data[folders[t]].append(d)

series = []
for folder in folders:
    pr_avg = util.average(data[folder], pr_key_y, pr_key_x)
    breakeven_points = util.find_breakeven(pr_avg, samples=30)
    print(folder, breakeven_points)
    series.append({
        "name": folder,
        "data": pr_avg,
        "breakeven": breakeven_points
    })
util.display_precision_recall_plot(series)

series = []
for folder in folders:
    loss_avg = util.average(data[folder], 'events', lc_key_x)
    print(folder, loss_avg[-1]['test_loss'])
    series.append({"name": folder, "data": loss_avg, "y_key": lc_key_y})
util.display_loss_curve_plot(series)