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05_analyze.py
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05_analyze.py
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# %% [markdown]
# # Technical background
# - A search of features for
# - k-fold (10) evaluation with unmixed persons
# - the overall specificity and sensitivity is calculed as a __minimal__ value of 10 folds
# - NSGA-II search
# - selects the window size in the gene
#
# # Issues and weaknesses
# - How to determine, if we found a specific feature, or some recording-specific noise? control and test dataset were captured using different device
#
#
# %%
# %pip install py-paretoarchive
import random
from galib import ChromosomeChannels, GAops
import svgutils.compose as svc
from galib import EEGschema
from mpl_toolkits.axes_grid1 import make_axes_locatable
import pickle
import gzip
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from paretoarchive.pandas import pareto
import re
import os
import glob
import parameters
# %%
alld = []
resdir = "res"
for fn in glob.glob(f"{resdir}/*.pkl.gz"):
population = pickle.load(gzip.open(fn))
log = open(fn.replace(".pkl.gz", ".log")).readlines()
eltime = np.nan
for l in log:
if l.startswith("Run arguments: "):
l = l.split("arguments: ")[1]
params = json.loads(l.replace("'", '"'))
if l.startswith("Elapsed time: "):
eltime = float(l.split(":")[-1].strip())
df = pd.DataFrame([{"chrom": str(p), "data": p, "elapsed": eltime,
"window_size": np.nan, **p.parameters, **params} for p in population]).sort_values(["features", "accuracy"])
df["logname"] = os.path.basename(fn).replace(".pkl.gz", "")
alld.append(df)
df = pd.concat(alld).reset_index().drop(columns=["index"])
df = df.query("classifier in ['kneighbors', 'svm']")
df
# %%
df["features_cnt"] = pd.cut(df["features"], bins=[3, 10, 20, 30, 40, 50, 60])
g = sns.displot(data=df,
x="sensitivity",
kind="hist",
col="classifier",
col_wrap=2,
col_order=["svm", "kneighbors"],
facet_kws=dict(sharex=False, sharey=False),
bins=15,
height=2,
aspect=1.5,
hue="features_cnt",
multiple="stack",
lw=0.2
)
g.set(
xlim=(0.85, 1),
ylim=(0, None)
)
g._legend.set_title("# features")
g.set_titles("{col_name}")
for i, ax in enumerate(g.axes.ravel()):
t = ["a)", "b)"][i]
ax.set_title(t + " " + parameters.classifier_titles[ax.get_title()])
g.fig.tight_layout(rect=(0, 0, 0.8, 1))
g.savefig("plt/featurescnt.pdf")
# %%
# pareto optimal soulutions over runs
allp = []
for r, dfr in df.query("features > 0").groupby("classifier"):
dfp = pareto(dfr, ["features", "accuracy", "sensitivity", "specificity"], minimizeObjective1=True,
minimizeObjective2=False, minimizeObjective3=False, minimizeObjective4=False)
allp.append(dfp)
dfp = pd.concat(allp)
dfp
# %%
# %%
fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(8, 4))
axis = {
# "dt": ax1,
# "ridge": ax2,
"svm": ax1,
"kneighbors": ax2
}
allpar = []
df_best = []
for classifier, dfr in df.query("features > 0").groupby("classifier"):
t = {"svm": "a)", "kneighbors": "b)"}[classifier]
dfpr = pareto(dfr, ["features", "accuracy", "sensitivity", "specificity"], minimizeObjective1=True,
minimizeObjective2=False, minimizeObjective3=False, minimizeObjective4=False)
allpar.append(dfpr)
dfp_glob = dfpr.sort_values(["sensitivity", "specificity", "features"], ascending=[
False, False, True]).reset_index(drop=True)
c = dfp_glob.loc[0]["data"]
df_best.append(dfp_glob.loc[0])
c.ops.channels = [x.replace("EEG ", "") for x in c.ops.channels]
ax = axis[classifier]
c.vizualize(ax)
ax.set(xlim=(0, 50))
ax.set_title(
f"{t} {parameters.classifier_titles[classifier]} ({c.parameters['sensitivity']:.2%}/{c.parameters['specificity']:.2%}/{c.parameters['features']:d})")
#fig.suptitle("Best solutions (Pareto-optimal)")
#fig.tight_layout(rect=(0, 0, .9, 1))
fig.tight_layout()
# plt.show()
fig.savefig("plt/bestsolutions.pdf")
# %%%
pd.DataFrame(df_best).drop(columns=["data", "window_size", "fit_time", "generations", "log",
"output_file", "logname", "features_cnt"]).to_excel("md_res/best_solutions.xlsx", index=False)
# %%
df.sort_values(["classifier", "sensitivity", "specificity", "features"], ascending=[False, False, False, True]).drop(columns=[
"data", "window_size", "fit_time", "generations", "log", "output_file", "logname", "features_cnt"]).to_excel("md_res/all_solutions.xlsx", index=False)
# %% [markdown]
# # Results
# %%
stats = {}
for cl in df["classifier"].unique():
stats[cl] = np.array([c.get_heat()
for c in df.query("classifier == @cl")["data"]])
chrom1 = dfp["data"].iloc[0]
stats
# %%
# table how many channels are used for a given freq
fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(8, 4))
axis = {
# "dt": ax1,
# "ridge": ax2,
"svm": ax1,
"kneighbors": ax2
}
for cl in df["classifier"].unique():
# stat_freq = stats[cl].sum(axis=1).sum(axis=1).mean(axis=0) # .any(axis=1) #.sum(axis=0)
stat_freq = np.logical_or.reduce(
np.logical_or.reduce(stats[cl], axis=1), axis=1).sum(axis=0)
stat_freq.shape
t = {"svm": "a)", "kneighbors": "b)"}[cl]
ax = axis[cl]
print(parameters.classifier_colors[cl])
ax.bar(chrom1.ops.freqs[:chrom1.ops.freqs_max],
stat_freq, color=parameters.classifier_colors[cl])
# #plt.show()
ax.set(
xlabel="Frequency (Hz)",
ylabel="# planes",
title=t + " " + parameters.classifier_titles[cl],
xlim=(0, 50),
ylim=(0, 32)
)
fig.tight_layout()
# stat_freq.shape
# #stat_channel.shape
# #pd.DataFrame(stat_channel, columns=chrom1.ops.channels, index=[chrom1.fn2str(i) for i in range(5)])
fig.savefig("plt/frequency.pdf")
# %%
# fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 7))
# axis = {
# "dt": ax1,
# "ridge": ax2,
# "svm": ax3,
# "kneighbors": ax4
# }
# for cl in df["classifier"].unique():
# stat_channel = stats.mean(axis=0) #sum(axis=1)
# max_freq = chrom1.ops.freqs[chrom1.ops.freqs_max]
# stat_channel.shape
# fig, axes = plt.subplots(3, 2, figsize=(16, 10))
# axes = axes.ravel()
# for i, ax in zip(range(chrom1.ops.fun_max), axes):
# ax.set_title("fun " + chrom1.fn2str(i))
# im = ax.imshow(stat_channel[i], aspect="auto", interpolation = "None", vmin=0,
# vmax=stat_channel.max(), extent=[0, max_freq, 0,
# chrom1.ops.channels_max])
# ax.set_yticks(np.arange(chrom1.ops.channels_max) + 0.5, chrom1.ops.channels)
# cax = plt.colorbar(im, ax=ax)
# cax.set_label("proportion in found filters")
# axes[-1].axis("off")
# plt.tight_layout()
# stat_channel.max()
# plt.show()
# %%
fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(8, 4))
axis = {
# "dt": ax1,
# "ridge": ax2,
"svm": ax1,
"kneighbors": ax2
}
max_freq = 50
for cl in df["classifier"].unique():
ax = axis[cl]
t = {"svm": "a)", "kneighbors": "b)"}[cl]
# stat_channel = stats[cl].mean(axis=0).sum(axis=0) # sum(axis=1)
stat_channel = np.logical_or.reduce(
stats[cl], axis=1).sum(axis=0).astype("f")
#stat_channel[stat_channel == 0] = np.nan
# if cl == "svm":
# stat_channel[stat_channel < 3] = np.nan
# if cl == "kneighbors":
# stat_channel[stat_channel < 5] = np.nan
img = ax.imshow(stat_channel, interpolation="none", origin="lower",
aspect="auto", extent=[0, max_freq, 0, chrom1.ops.channels_max],
vmin=0, rasterized=True)
ax.set_yticks(np.arange(chrom1.ops.channels_max) + 0.5,
[x.replace("EEG ", "") for x in chrom1.ops.channels])
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.05)
cb = fig.colorbar(img, cax=cax, orientation='vertical')
#cax = plt.colorbar(img)
cb.set_label("# planes")
ax.set(
xlabel="Frequency (Hz)",
title=t + " " + parameters.classifier_titles[cl]
)
stat_channel.shape
fig.tight_layout()
fig.savefig("plt/features.pdf")
fig.savefig("plt/features.png", dpi=300)
# %%%
max_freq = 50
vmax = 0.625
for cl in df["classifier"].unique():
# cl = "svm"
t = {"svm": "a)", "kneighbors": "b)"}[cl]
print("CL is ", cl)
stat_chan = np.logical_or.reduce(np.logical_or.reduce(
stats[cl], axis=1), axis=2).sum(axis=0).astype("f")
print(stats[cl].shape)
print("max = ", stat_chan.max())
chmax = stat_chan.max()
stat_chan /= stats[cl].shape[0]
stat_chan /= stat_chan.max()
sch = EEGschema()
cm = plt.get_cmap("viridis")
for ch, c in zip(chrom1.ops.channels, stat_chan):
#print(ch, c)
sch.set_channel_color(ch.replace(
"EEG ", "").replace("-LE", "").lower(), cm(c))
sch.savefig(f"usage_{cl}.svg")
vmax = 1
a = np.array([[0, chmax]])
plt.figure(figsize=(4, 0.5))
img = plt.imshow(a, cmap="viridis")
plt.gca().set_visible(False)
cax = plt.axes([0.1, 0.2, 0.8, 0.6])
plt.colorbar(orientation="horizontal", cax=cax, label="# planes")
plt.savefig(f"usage_bar_{cl}.svg")
sch
# %%%
vmax = 1
a = np.array([[0, vmax * 100]])
plt.figure(figsize=(9, 1.0))
img = plt.imshow(a, cmap="viridis")
plt.gca().set_visible(False)
cax = plt.axes([0.1, 0.2, 0.8, 0.6])
plt.colorbar(orientation="horizontal", cax=cax, label="Occurance [%]")
plt.savefig("usage_bar.svg")
# %%%
# s = svc.Figure("310mm", "180mm",
# svc.Panel(
# svc.Text("k-NN", 70, 6, size=6),
# svc.SVG("usage_kneighbors.svg").move(0, 8),
# ), svc.Panel(
# svc.Text("SVM", 70, 6, size=6),
# svc.SVG("usage_svm.svg").move(0, 8)
# ).move(150, 0),
# svc.Panel(
# svc.SVG("usage_bar.svg", fix_mpl=True).scale(
# 0.3).move(60, 150)
# )
# )
s = svc.Figure("300mm", "175mm", svc.Panel(
svc.Text("SVM", 69, 6, size=6),
svc.SVG("usage_svm.svg").move(0, 8),
svc.SVG("usage_bar_svm.svg", fix_mpl=True).scale(0.4).move(17, 145)
),
svc.Panel(
svc.Text("k-NN", 69, 6, size=6),
svc.SVG("usage_kneighbors.svg").move(0, 8),
svc.SVG("usage_bar_kneighbors.svg", fix_mpl=True).scale(0.4).move(17, 145)
).move(150, 0),
# svc.Panel(
# svc.SVG("usage_bar.svg", fix_mpl=True).scale(
# 0.3).move(60, 150)
# )
)
s.save("usage_all.svg")
os.system("inkscape usage_all.svg --export-filename=usage_all.pdf")
s
# %%
ops = GAops([f"CH{x+1:02d}" for x in range(8)], np.arange(0, 513) / 4)
fig = plt.figure(figsize=(8, 4))
ax1 = plt.subplot2grid((8, 1), (0, 0), rowspan=6)
ax2 = plt.subplot2grid((8, 1), (7, 0))
random.seed(42)
np.random.seed(60)
ch = ChromosomeChannels(ops).random(6)
ch.vizualize(ax=ax1)
chrom = str(ch)
chrom = chrom.replace(" ", "")
chrom = re.sub("(.{64}.*?,)", "\\1\n", chrom, 0, re.DOTALL)
ax2.text(0, 0, chrom, font="monospace")
ax2.axis("off")
#ax1.arrow(0.25, 0.2, 0.11, 0.22, transform=fig.transFigure, clip_on=False, head_width=0.02, edgecolor="tab:red", fc="tab:red")
ax1.annotate("", xytext=(20, -1.5), xy=(40, 1.5), clip_on=False,
c="tab:red", arrowprops=dict(arrowstyle="-|>,head_width=0.4,head_length=0.8",
shrinkA=0, shrinkB=1, fc="tab:red", ec="tab:red"))
ax1.annotate("", xytext=(50, -1.5), xy=(6, 4.5), clip_on=False,
c="tab:red", arrowprops=dict(arrowstyle="-|>,head_width=0.4,head_length=0.8",
shrinkA=0, shrinkB=1, fc="tab:red", ec="tab:red"))
str(ch)
plt.tight_layout()
plt.savefig("plt/chromosome.pdf")
# %%
dft = df.groupby("classifier").agg({
"elapsed": "mean",
"sensitivity": ("min", "mean", "max"),
"specificity": ("min", "mean", "max"),
"features": ("min", "mean", "max"),
}).reset_index()
dft["classifier"] = dft["classifier"].apply(
lambda x: parameters.classifier_titles[x])
dft
# %%%
style = dft.style.format(
"{:.1%}", subset=["sensitivity", "specificity"], escape="latex"
).format("{:.2f}", subset=[["features", "mean"]]
).format(precision=2, subset="elapsed"
).hide(axis="index")
print(style.to_latex().replace("%", "\\%"))
# %%
df_dt = pd.read_pickle("test.pkl.gz")
df_dt
# %%%
# from galib import ChromosomeBase, ChromosomeChannels, FeaturesException, GAops, DataParser
# import sklearn.neighbors
# import sklearn.linear_model
# import sklearn.tree
# import sklearn.svm
# from sklearn.model_selection import GroupKFold, cross_validate, train_test_split
# from sklearn.metrics import accuracy_score, confusion_matrix
# def sas_score(estimator, X_test, y_test):
# y_pred = estimator.predict(X_test)
# conf_m = confusion_matrix(y_test, y_pred).ravel()
# if len(conf_m) != 4: # wrong test data
# return {
# "specificity": 0,
# "accuracy": 0,
# "sensitivity": 0,
# }
# tn, fp, fn, tp = conf_m
# r = {
# "specificity": 0 if tn == 0 else (tn) / (fp + tn), # specificity"
# "accuracy": (tp + tn) / (tp + fp + tn + fn), # accuracy
# "sensitivity": 0 if tp == 0 else (tp) / (tp + fn), # sensitivity"
# }
# return r
# data_parser = DataParser()
# cv = GroupKFold(5)
# def evaluate_chromosome(chrom):
# global cv
# data = data_parser.get_data()
# X = data["x"]
# y = data["y"]
# samples_id = data["id"]
# freqs = data["freqs"]
# channels = data["features"]
# model = sklearn.tree.DecisionTreeClassifier(random_state=42)
# X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)
# try:
# cv = cross_validate(model, chrom.execute(X), y,
# groups=samples_id, cv=cv,
# scoring=sas_score, n_jobs=-1
# )
# model.fit(chrom.execute(X_train), y_train)
# print(model.score(chrom.execute(X_test), y_test))
# except FeaturesException as e:
# print("Features exception: {} for chromosome {}".format(e, chrom))
# cv = {
# "test_accuracy": np.array([0.0]),
# "test_specificity": np.array([0.0]),
# "test_sensitivity": np.array([0.0])
# }
# aggfunc = np.mean
# score = {
# "accuracy": aggfunc(cv["test_accuracy"]),
# "sensitivity": aggfunc(cv["test_sensitivity"]),
# "specificity": aggfunc(cv["test_specificity"]),
# "features": chrom.features,
# }
# return score, model
# chrom = df_dt[3]
# chrom
# e, m = evaluate_chromosome(chrom)
# e, m
# # %%
# from sklearn.tree import plot_tree
# plot_tree(m)
# %%