File size: 6,906 Bytes
4ca4e4c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | import numpy as np
import torch
import matplotlib.pyplot as plt
from collections import defaultdict
import os
# colors = {"TF": {"TF": "blue", "SSM": "orange"}, "SSM": {"TF": "green", "SSM": "red"}, "TF-nC": {"TF-nC": "brown"}}
colors = {"TF_TF": "blue", "TF_SSM": "orange", "SSM_TF": "green", "SSM_SSM": "red", "TF-nC_TF-nC": "brown"}
def Int(s): return int("".join([c for c in s if c.isnumeric()]))
def Empty(): return []
def get_val_and_bounds(data):
mean = np.mean(data, axis=0)
median = np.median(data, axis=0)
# return mean, np.min(data, axis=0), np.max(data, axis=0)
# return mean, mean-np.std(data, axis=0), mean+np.std(data, axis=0)
# return median, np.min(data, axis=0), np.max(data, axis=0)
return median, np.quantile(data, 0.10, axis=0), np.quantile(data, 0.90, axis=0)
def savefig(taskname, filename):
if "fig" not in os.listdir("results/" + taskname):
os.mkdir("results/" + taskname + "/fig")
plt.savefig("results/" + taskname + "/fig/" + filename + ".png")
split_array = ['_', 'task_name', 'layer1', 'layer2', 'window', 'dim', 'num_heads', 'state_dim']
def plot(data, params, ind_var, diff_lines="layers", param_counts=None, x_axis=None):
fig, ax = plt.subplots()
if x_axis == 'epochs':
xs = defaultdict(Empty)
ys = defaultdict(Empty)
ys_lower = defaultdict(Empty)
ys_upper = defaultdict(Empty)
# Get the relevant data for these params
for k in data.keys():
d = dict(zip(split_array, k.split('_')))
if diff_lines != 'layer1' and params['layer1'] != d['layer1']: continue
if diff_lines != 'layer2' and params['layer2'] != d['layer2']: continue
if diff_lines != 'window' and params['window'] != Int(d['window']): continue
if diff_lines != 'dim' and params['dim'] != Int(d['dim']): continue
if diff_lines != 'num_heads' and params['num_heads'] != Int(d['num_heads']): continue
if diff_lines != 'state_dim' and params['state_dim'] != Int(d['state_dim']): continue
key = Int(d[diff_lines])
xs[key] = np.arange(0, data[k].shape[1])
ys[key], ys_lower[key], ys_upper[key] = get_val_and_bounds(data[k])
legend = []
keys = sorted(list(ys.keys()))
for key in keys:
plt.plot(xs[key], ys[key])
legend.append(key)
plt.legend(legend)
for key in ys.keys():
plt.fill_between(xs[key], ys_lower[key], ys_upper[key], color='lightblue', alpha=0.08)
elif diff_lines == 'layers':
xs = defaultdict(Empty)
ys = defaultdict(Empty)
ys_lower = defaultdict(Empty)
ys_upper = defaultdict(Empty)
# Get the relevant data for these params
for k in data.keys():
d = dict(zip(split_array, k.split('_')))
if ind_var != 'window' and params['window'] != Int(d['window']): continue
if ind_var != 'dim' and params['dim'] != Int(d['dim']): continue
if ind_var != 'num_heads' and params['num_heads'] != Int(d['num_heads']): continue
if ind_var != 'state_dim' and params['state_dim'] != Int(d['state_dim']): continue
key = d['layer1'] + '_' + d['layer2']
if d['layer2'].isnumeric():
print("Ignoring", key)
continue
if x_axis == 'params':
xs[key].append(param_counts[k])
else:
xs[key].append(Int(d[ind_var]))
r1, r2, r3 = get_val_and_bounds(data[k][:, -1])
ys[key].append(r1)
ys_lower[key].append(r2)
ys_upper[key].append(r3)
# Sort the data so it is in order on the x axis
for key in ys.keys():
ys[key] = [a[1] for a in sorted(zip(xs[key], ys[key]))]
ys_lower[key] = [a[1] for a in sorted(zip(xs[key], ys_lower[key]))]
ys_upper[key] = [a[1] for a in sorted(zip(xs[key], ys_upper[key]))]
xs[key].sort()
# Plot the lines
legend = []
for key in ys.keys():
if key == "SSM_SSM" and ind_var == "num_heads" or key == "TF_TF" and ind_var == "state_dim":
plt.axhline(y=ys[key][0], color=colors[key], linestyle='dashed')
else:
plt.plot(xs[key], ys[key], c=colors[key])
legend.append(key)
plt.legend(legend)
# Plot the error bars
for key in ys.keys():
if key == "SSM_SSM" and ind_var == "num_heads" or key == "TF_TF" and ind_var == "state_dim":
plt.fill_between(ax.get_xlim(), ys_lower[key][0], ys_upper[key][0], color=colors[key], alpha=0.08)
else:
plt.fill_between(xs[key], ys_lower[key], ys_upper[key], color=colors[key], alpha=0.08)
elif diff_lines == 'depths':
assert False # TODO: Doesn't plot across depth
xs = defaultdict(Empty)
ys = defaultdict(Empty)
ys_lower = defaultdict(Empty)
ys_upper = defaultdict(Empty)
# Get the relevant data for these params
for k in data.keys():
d = dict(zip(split_array, k.split('_')))
if ind_var != 'window' and params['window'] != Int(d['window']): continue
if ind_var != 'dim' and params['dim'] != Int(d['dim']): continue
if ind_var != 'num_heads' and params['num_heads'] != Int(d['num_heads']): continue
if ind_var != 'state_dim' and params['state_dim'] != Int(d['state_dim']): continue
key = d['layer1'] + '_' + d['layer2']
if x_axis == 'params':
xs[key].append(param_counts[key])
else:
xs[key].append(Int(d[ind_var]))
r1, r2, r3 = get_val_and_bounds(data[k][:, -1])
ys[key].append(r1)
ys_lower[key].append(r2)
ys_upper[key].append(r3)
# Sort the data so it is in order on the x axis
for key in ys.keys():
ys[key] = [a[1] for a in sorted(zip(xs[key], ys[key]))]
ys_lower[key] = [a[1] for a in sorted(zip(xs[key], ys_lower[key]))]
ys_upper[key] = [a[1] for a in sorted(zip(xs[key], ys_upper[key]))]
xs[key].sort()
# Plot the lines
legend = []
for key in ys.keys():
plt.plot(xs[key], ys[key], c=colors[key.split("_")[0] + "_" + key.split("_")[0]])
legend.append(key)
plt.legend(legend)
# Plot the error bars
for key in ys.keys():
plt.fill_between(xs[key], ys_lower[key], ys_upper[key], color=colors[key.split("_")[0] + "_" + key.split("_")[0]], alpha=0.08)
return diff_lines + "_" + ind_var |