Blame view
volia/stats.py
3.34 KB
9a2c6b4d0
New file that hel...
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
import argparse
import os
import core.data
import math
import numpy as np
import scipy.stats
import pickle
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from cycler import cycler
def stats():
print("Decisions")
print(list(mcolors.TABLEAU_COLORS))
if __name__ == "__main__":
# Parser
parser = argparse.ArgumentParser(description="")
# Arguments
parser.add_argument("--predictions", type=str, help="prediction file", required=True)
parser.add_argument("--labels", type=str, help="label file", required=True)
parser.add_argument("--labelencoder", type=str, help="label encode pickle file", required=True)
parser.add_argument("--outdir", type=str, help="output file", required=True)
args = parser.parse_args()
predictions = core.data.read_id_values(args.predictions, float)
labels = core.data.read_labels(args.labels)
le = None
with open(args.labelencoder, "rb") as f:
le = pickle.load(f)
stats = {}
print("PREDICTIONS ---------------------------")
for id_, predictions_ in predictions.items():
label = labels[id_][0]
if label not in stats:
stats[label] = {
"nb_utt": 1,
"predictions": np.expand_dims(predictions_, axis=0)
}
else:
stats[label]["nb_utt"] = stats[label]["nb_utt"] + 1
stats[label]["predictions"] = np.append(stats[label]["predictions"], np.expand_dims(predictions_, axis=0), axis=0)
print("CALCULATING ---------------------------")
colors = [
"darkorange",
"red",
"blue"
]
custom_cycler = (cycler(color=list(mcolors.TABLEAU_COLORS)) *
cycler(linestyle=['-', '--', '-.']))
for label, stats_ in stats.items():
plt.gca().set_prop_cycle(custom_cycler)
stats_mean = np.mean(stats_["predictions"], axis=0)
stats_std = np.std(stats_["predictions"], axis=0)
#print(label)
#print(stats_mean)
#print(stats_std)
kwargs = dict(alpha=0.5)
for i in range(stats_["predictions"].shape[1]):
label_str = le.inverse_transform([i])[0]
#plt.hist(stats_["predictions"][:, i], bins=10, label=label_str, **kwargs)
mu = stats_mean[i]
variance = stats_std[i] * stats_std[i]
sigma = stats_std[i]
# math.sqrt(variance)
print(f"{i}: mu {mu}, var {variance}, sigma {sigma}")
#x_values = np.arange(-1, 5, 0.1)
#y_values = scipy.stats.norm(mu, variance)
#y = scipy.stats.norm.pdf(x,mean,std)
#plt.plot(x_values, y_values.pdf(x_values,))
#x, step = np.linspace(mu - 3*sigma, mu + 3*sigma, 1000, retstep=True)
x = np.linspace(0, 1, 1000)
#x = np.linspace(mu - 3*sigma, mu + 3*sigma, 1000)
#x, step = np.linspace(0, 1, 1000, retstep=True)
P = scipy.stats.norm.cdf(x, mu, sigma)
#print(step)
plt.plot(x, P, label=label_str, **kwargs)
#plt.savefig("simple_gaussian.pdf")
plt.legend()
plt.savefig(os.path.join(args.outdir, f"{label}_prediction_cdf.pdf"))
plt.clf()
# TODO:
# One graph for each label. Distribution of their predictions output are displayed.
|