show_csv Namespace Reference

Functions
def	print_tabular ()
def	plot_tabular ()

Variables
	parser = argparse.ArgumentParser()
	help
	required
	type
	int
	default
	bool
	str
	args = parser.parse_args()
	path = args.out
tuple	figsize = (7, 8)
	cols = list(pd.read_csv(args.path, nrows=1))
list	filter_in = [re.compile('[\w]*_weight'), re.compile('accuracy'), re.compile('pool_delay')]
	csv_data = pd.read_csv(args.path, usecols=[i for i in cols if any(re.match(fil, i) for fil in filter_in)])
list	data = []
	uni_accuracies = np.unique(data[-1])

Function Documentation

def show_csv.plot_tabular

(

)

Definition at line 103 of file show_csv.py.

def plot_tabular():
    for idx in range(len(cols)):
        for idx2 in range(idx + 1, len(cols)):
            unique1 = np.unique(data[idx])
            unique2 = np.unique(data[idx2])
            if len(unique2) < len(unique1):
                tmp = idx
                idx = idx2
                idx2 = tmp
                tmp = unique1
                unique1 = unique2
                unique2 = tmp
            fig, axs = plt.subplots(nrows=len(unique2), ncols=len(unique1), sharex='all', sharey='all', figsize=figsize)
            fig.suptitle(f'↓ {cols[idx2]} → {cols[idx]}')
            for uni1_idx, uni1 in enumerate(unique1):
                for uni2_idx, uni2 in enumerate(unique2):
                    acc_sum = list(np.zeros(uni_accuracies.shape))
                    for ind, acc in enumerate(uni_accuracies):
                        count_acc = [True for dat in data.transpose()
                                     if dat[idx] == uni1 and dat[idx2] == uni2 and dat[-1] == acc]
                        acc_sum[ind] = len(count_acc)
                    avg_acc = uni_accuracies - uni_accuracies[0]
                    avg_acc = avg_acc/uni_accuracies[-1]
                    avg_acc = list(map(mul, acc_sum, avg_acc))
                    avg_acc = sum(avg_acc)/sum(acc_sum)
                    acc_sum = np.cumsum(acc_sum)
                    hue = (-(1/3)+avg_acc/3)+1
                    hsv_colors = (hue, 1, 1)
                    ax = axs[uni2_idx][uni1_idx]
                    ax.plot(uni_accuracies, acc_sum, c=mpc.hsv_to_rgb(hsv_colors))
                    ax.grid()
                    if uni2_idx == 0:
                        ax.set_title(f'{uni1:.4f}')
                    if uni1_idx == 0:
                        ax.set_ylabel(f'{uni2:.4f}')
            os.makedirs(path, exist_ok=True)
            plt.savefig(f'{path}/{cols[idx2]}_{cols[idx]}.png', bbox_inches='tight')

            if idx2 < idx:
                tmp = idx
                idx = idx2
                idx2 = tmp


print_tabular()
plot_tabular()

def show_csv.print_tabular

(

)

Definition at line 45 of file show_csv.py.

def print_tabular():
    if args.group:
        best_accuracy = uni_accuracies[-1]
        upper_thresh = np.floor(best_accuracy*10)/10
        lower_thresh = upper_thresh - 0.1 * (args.num - 1) - 0.0001
        i = lower_thresh
        best_accs = []
        while i < upper_thresh:
            j = i
            # avoid floating point error like 0.70001, which doesn't include 0.7 anymore
            i += 0.099998
            best_accs.append([acc for acc in uni_accuracies if j <= acc < i])
    else:
        best_accs = [[acc] for acc in uni_accuracies[-args.num:]]
    indent = max(8, 4+2*args.num)

    word = 'accuracy groups' if args.group else 'accuracies'

    print(f'Count of best {args.num} {word} in the tables below from right to left:')
    if not args.group:
        print([x[0] for x in best_accs])
    else:
        print([(x[0], x[-1]) for x in best_accs])
    if not args.group:
        print(f'0+1+2 means 0 occurrences of {best_accs[0]}, 1 occurrence of {best_accs[1]} and 2 occurrences of '
              f'{best_accs[2]} for this specific parameters')
    else:
        print(f'0+1+2 means 0 occurrences of {best_accs[0][0]}-{best_accs[0][-1]}, ', end='')
        if len(best_accs) > 1:
            print(f'1 occurrence of {best_accs[1][0]}-{best_accs[1][-1]} ', end='')
        if len(best_accs) > 2:
            print(f'and 2 occurrences of {best_accs[2][0]}-{best_accs[2][-1]} ', end='')
        print(f'for this specific parameters')
    print('')
    for idx in range(len(cols)):
        for idx2 in range(idx + 1, len(cols)):
            print(f'↓ {cols[idx]} → {cols[idx2]}')
            unique1 = np.unique(data[idx])
            unique2 = np.unique(data[idx2])
            print(f"{'':>{8}}", end='')
            for uni2 in unique2:
                print(f'{uni2.round(5):>{indent}}', end='')
            print('')
            for uni1 in unique1:
                print(f'{uni1.round(5):>8}', end='')
                for uni2 in unique2:
                    acc = [d[-1] for d in data.transpose() if d[idx] == uni1 and d[idx2] == uni2]
                    counts = []
                    for best_acc in best_accs:
                        counts.append(sum([x in best_acc for x in acc]))
                    count_str = ''
                    for count in counts:
                        count_str += str(count) + '+'
                    print(f'{count_str[:-1]:>{indent}}', end='')
                print('')
            print('')

Variable Documentation

show_csv.args = parser.parse_args()

Definition at line 18 of file show_csv.py.

show_csv.bool

Definition at line 16 of file show_csv.py.

list show_csv.cols = list(pd.read_csv(args.path, nrows=1))

Definition at line 29 of file show_csv.py.

show_csv.csv_data = pd.read_csv(args.path, usecols=[i for i in cols if any(re.match(fil, i) for fil in filter_in)])

Definition at line 32 of file show_csv.py.

show_csv.data = []

Definition at line 35 of file show_csv.py.

show_csv.default

Definition at line 15 of file show_csv.py.

tuple show_csv.figsize = (7, 8)

Definition at line 26 of file show_csv.py.

list show_csv.filter_in = [re.compile('[\w]*_weight'), re.compile('accuracy'), re.compile('pool_delay')]

Definition at line 31 of file show_csv.py.

show_csv.help

Definition at line 14 of file show_csv.py.

show_csv.int

Definition at line 15 of file show_csv.py.

show_csv.parser = argparse.ArgumentParser()

Definition at line 13 of file show_csv.py.

show_csv.path = args.out

Definition at line 25 of file show_csv.py.

show_csv.required

Definition at line 14 of file show_csv.py.

show_csv.str

Definition at line 17 of file show_csv.py.

show_csv.type

Definition at line 15 of file show_csv.py.

show_csv.uni_accuracies = np.unique(data[-1])

Definition at line 42 of file show_csv.py.

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