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figures.py
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figures.py
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from gccr002 import *
SAVE = True
CONTEXT = None
def save_fig(name):
dpi = 300
formats = ['png', 'pdf', 'tif', 'eps']
if True:
path = pathlib.Path('figures')
if isinstance(CONTEXT, int):
path = path / ('Figure %d' % CONTEXT)
elif isinstance(CONTEXT, str):
if CONTEXT.isnumeric():
path = path / ('Figure %s' % CONTEXT)
elif CONTEXT[0]=='S' and CONTEXT[1:].isnumeric():
path = path / ('Figure %s' % CONTEXT)
else:
path = path / CONTEXT
path.mkdir(exist_ok=True, parents=True)
with all_logging_disabled():
for format_ in formats:
if format_ == 'tif':
kwargs = {"pil_kwargs": {"compression": "tiff_lzw"}}
else:
kwargs = {}
# Some versions require string conversion of path
path_ = str(path / ('%s.%s' % (name, format_)))
plt.savefig(path_, format=format_, bbox_inches="tight", **kwargs)
def auto_save_fig(func):
def wrapper(*args, **kwargs):
func(*args, **kwargs)
if SAVE:
jsonable = [x for x in args if isinstance(x, (int, str))]
jsonable += [x for x in kwargs.values() if isinstance(x, (int, str))]
h = hashlib.sha1(json.dumps(jsonable).encode("utf-8")).hexdigest()[:10]
save_fig('%s_%s' % (func.__name__, h))
return wrapper
@auto_save_fig
def lab_vs_clinical(df, s, senses):
fig, axes = plt.subplots(2, 2, figsize=(12, 8), sharey=True)
ax = axes.flat
for i, sense in enumerate(senses):
features = [
"%s_%s" % (sense, when) for when in ["before_illness", "during_illness"]
]
only = s["lab-covid"] | s["clinical-covid"]
z = df.loc[only].melt(id_vars=["id", "COVID Status (All)"], value_vars=features)
sns.violinplot(
data=z,
x="variable",
y="value",
hue="COVID Status (All)",
inner="quartile",
ax=ax[i],
orient="v",
split=True,
cut=0,
palette={
"C19+": colors.loc["C19+", sense],
"C19+ (Clinical)": colors.loc["C19-", sense],
},
)
fix_quartile_lines(ax[i])
nicify_labels(ax[i])
ax[i].set_xlabel("")
ax[i].set_ylabel("Rating" if i == 0 else "", fontweight="bold")
l = ax[i].legend(loc=(0.3, 1.02), fontsize=10)
l.get_texts()[0].set_text("C19+ (Lab)")
ax[i].set_yticks(np.linspace(0, 100, 6))
ax[0].set_yticklabels(np.linspace(0, 100, 6).astype(int))
ax[0].set_ylim(0, 100)
plt.tight_layout()
@auto_save_fig
def pc_senses(df, s, senses):
features = ["%s_change_illness" % sense for sense in senses]
z = df.loc[s["lab-covid"] | s["clinical-covid"], features]
pca = PCA(2)
pca.fit(z)
sns.set_style("ticks")
plt.figure(figsize=(4, 4))
circle = plt.Circle((0, 0), 1, fill=True, color="lightgray", alpha=0.5)
for i, sense in enumerate(senses):
x, y = pca.components_[:, i]
r = np.sqrt(x ** 2 + y ** 2)
color = colors["C19+", sense]
plt.arrow(
0,
0,
x / r,
y / r,
color=color,
width=0.03,
length_includes_head=True,
alpha=0.5,
)
plt.plot([0, 0], [0, 0], c=color, label=nicify(features[i]))
plt.gca().add_artist(circle)
plt.xlim(-1.2, 1.2)
plt.ylim(-1.2, 1.2)
plt.legend(fontsize=8, loc=(0, 1.05))
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
@auto_save_fig
def senses_summary(df, s, senses, left=["before", "during"], right=['change']):
fig, ax = plt.subplots(4, 2, figsize=(6, 10))
for i, sense in enumerate(senses):
features = [
"%s_%s_illness" % (sense, when) for when in left
]
# for j, status in enumerate(['lab-covid', 'non-covid']):
only = df.index.intersection(s["lab-covid"] | s["non-covid"])
z = df.loc[only].melt(id_vars=["COVID Status"], value_vars=features)
sns.pointplot(
data=z,
y="value",
x="variable",
hue="COVID Status",
ax=ax[i, 0],
scale=1.25,
markersize=25,
palette={"C19+": colors.loc["C19+", sense], "C19-": colors.loc["C19-", sense]},
)
c19plus = df.loc[only & s["lab-covid"], features[1]].dropna()
c19minus = df.loc[only & s["non-covid"], features[1]].dropna()
_, p = mannwhitneyu(c19plus, c19minus)
print('%s (%s): C19+ = %.3g +/- %.3g; C19- = %.3g +/- %.3g; p=%.3g' %
(nicify(sense), left[1], c19plus.mean(), c19plus.std(), c19minus.mean(), c19minus.std(), p))
zeros = int(-np.log10(p))
#stars = "*" * (np.clip(zeros, 2, 5) - 2)
#ax[i, 0].text(
# 1.1, (c19plus.mean() + c19minus.mean()) / 2, stars, va="center", fontsize=18
#)
# Draw a seaborn pointplot onto each Axes
ax[i, 0].set_xlabel(nicify(sense))
ax[i, 0].set_xticklabels([nicify(x).replace(' illness', '').replace(' ', '\n') for x in left])
ax[i, 0].set_ylabel("Rating" if i == 0 else "")
ax[i, 0].legend(loc=1, fontsize=9)
ax[i, 0].set_yticks(np.linspace(0, 100, 6))
features = [
"%s_%s_illness" % (sense, when) for when in right
] # , 'recovery_illness']]
c19plus = df.loc[only & s["lab-covid"], features[-1]].dropna()
c19minus = df.loc[only & s["non-covid"], features[-1]].dropna()
_, p = mannwhitneyu(c19plus, c19minus)
print('%s (%s): C19+ = %.3g +/- %.3g; C19- = %.3g +/- %.3g; p=%.3g' %
(nicify(sense), right[-1], c19plus.mean(), c19plus.std(), c19minus.mean(), c19minus.std(), p))
z = df.loc[only].melt(id_vars=["id", "COVID Status"], value_vars=features)
sns.violinplot(
data=z,
x="variable",
y="value",
hue="COVID Status",
cut=0,
ax=ax[i, 1],
orient="v",
split=False,
palette={"C19+": colors.loc["C19+", sense], "C19-": colors.loc["C19-", sense]},
)
nicify_labels(ax[i, 1])
ax[i, 1].set_xlabel("")
ax[i, 1].set_ylabel("Δ Rating" if i == 0 else "", fontweight="bold")
ax[i, 1].legend().remove()
ax[i, 1].set_yticks(np.linspace(-100, 100, 11))
ax[i, 0].set_yticklabels(np.linspace(0, 100, 6).astype(int))
ax[i, 0].set_ylim(0, 100)
ax[i, 1].set_yticklabels(np.linspace(-100, 100, 11).astype(int))
ax[i, 1].set_ylim(-100, 100)
ax[0, 1].set_title('C19+ C19-')
plt.tight_layout()
def alt_senses_summary(df, s, senses):
fig, ax = plt.subplots(1, 4, figsize=(24, 4), sharey=True)
for i, sense in enumerate(senses):
features = [
"%s_%s" % (sense, when)
for when in ["before_illness", "during_illness", "after_illness"]
]
only = s["lab-covid"] | s["clinical-covid"] | s["non-covid"]
z = df.loc[only].melt(id_vars=["id", "COVID Status"], value_vars=features)
sns.violinplot(
data=z,
x="variable",
y="value",
hue="COVID Status",
cut=0,
ax=ax[i],
orient="v",
split=True,
inner="quartile",
palette={"C19+": colors.loc["C19+", sense], "C19-": colors.loc["C19-", sense]},
)
nicify_labels(ax[i])
fix_quartile_lines(ax[i])
ax[i].set_xlabel("")
ax[i].set_ylabel("Rating" if i == 0 else "", fontweight="bold")
ax[i].legend(loc=[0.3, 1.02], fontsize=9)
ax[i].set_yticks(np.linspace(0, 100, 6))
ax[0].set_yticklabels(np.linspace(0, 100, 6).astype(int))
ax[0].set_ylim(0, 100)
@auto_save_fig
def one_sense_all_diagnoses(df, sense):
sns.set_style("ticks")
palette = {
"C19+": colors.loc["C19+", "Smell"],
"C19+ (Clinical)": colors.loc["C19-", "Smell"],
"Unknown": "dimgray",
"C19-": "lightgray",
}
plt.figure(figsize=(8, 4))
ax = sns.violinplot(
data=df,
x="COVID Status (All)",
y="%s_during_illness" % sense.title(),
hue="COVID Status (All)",
orient="v",
palette=palette,
cut=0,
dodge=False,
inner="quartile",
)
fix_quartile_lines(ax)
ax.set_yticks(np.linspace(0, 100, 6))
ax.set_yticklabels(np.linspace(0, 100, 6).astype(int))
ax.set_ylabel("%s During Illness" % sense.title(), fontweight="bold")
ax.set_xlabel("")
ax.legend().remove() # loc=[1.03, 0.7], fontsize=10);
ax.text(
-1.15,
0,
"Total\n%s Change" % sense.title(),
ha="center",
va="center",
fontsize=10,
fontstyle="italic",
)
ax.text(
-1.15,
100,
"No\n%s Loss" % sense.title(),
ha="center",
va="center",
fontsize=10,
fontstyle="italic",
)
ax.set_ylim(0, 100)
@auto_save_fig
def effect_size_comparison(df, diagnoses, sense, direction):
feature = '%s_%s_illness' % (sense, direction)
diagnoses = {key: value for key, value in diagnoses.items() if key in df['COVID Status (All)'].values}
z = feature_compare(df, diagnoses, feature)
d = z.reorder_levels([1, 0], axis=1)["D"].astype("float")
for i in range(4):
for j in range(i, 4):
d.iloc[i, j] = None
Δ = z.reorder_levels([1, 0], axis=1)["Δ"].astype("float")
for i in range(4):
for j in range(i, 4):
Δ.iloc[i, j] = None
sns.set_style("white")
fig, ax = plt.subplots(1, 2, figsize=(11, 5))
sns.heatmap(d.iloc[1:, :3], cmap="Greens", vmin=0, vmax=1, annot=True, ax=ax[0])
sns.heatmap(Δ.iloc[1:, :3], cmap="Greens", vmin=0, vmax=25, annot=True, ax=ax[1])
ax[0].set_title("Effect size", pad=15, fontweight="bold")
ax[1].set_title("Mean difference", pad=15, fontweight="bold")
for a in ax:
a.set_ylabel("")
plt.tight_layout()
@auto_save_fig
def numeric_by_categorical(df, s, numeric_feature, categorical_feature, restrict=None):
only = (s["lab-covid"] | s["non-covid"])
if restrict:
only = only & restrict
ax = sns.violinplot(
x=categorical_feature,
y=numeric_feature,
data=df.loc[only],
split=True,
inner="quartile",
hue="COVID Status",
palette={"C19+": colors.loc["C19+", "Smell"], "C19-": colors.loc["C19-", "Smell"]},
cut=0,
scale_hue=0,
)
fix_quartile_lines(ax)
ax.set_yticks(np.linspace(0, 100, 6))
ax.set_yticklabels(np.linspace(0, 100, 6))
ax.set_ylabel(nicify(numeric_feature), fontweight="bold")
ax.legend(loc=9)
ax.set_xlabel("")
if categorical_feature == 'Gender':
ax.set_xticklabels(["Women", "Men"])
ax.set_ylim(0, 100)
@auto_save_fig
def numeric_by_status(df, s, numeric_feature):
df["junk"] = 1
only = s["lab-covid"] | s["non-covid"]
ax = sns.violinplot(
x="junk",
y=numeric_feature,
data=df.loc[only],
split=True,
inner="quartile",
palette={"C19+": colors.loc["C19+", "Smell"], "C19-": colors.loc["C19-", "Smell"]},
hue="COVID Status",
cut=0,
)
fix_quartile_lines(ax)
ax.set_xticks([])
ax.set_xlabel("")
ax.set_ylabel("Age", fontweight="bold")
ax.set_yticks(np.linspace(0, 100, 6))
ax.set_yticklabels(np.linspace(0, 100, 6).astype(int))
ax.set_ylim(18, 80)
ax.legend(loc=1)
df = df.drop('junk', axis=1)
@auto_save_fig
def numeric_by_status_split(df, s, numeric_feature, split_feature, restrict):
only = s["lab-covid"] | s["non-covid"]
if split_feature == "Gender":
palette = {"F": "darkorange", "M": "#00AA00"}
else:
palette = None
if restrict:
only = only & restrict
ax = sns.violinplot(
x="COVID Status",
y=numeric_feature,
data=df.loc[only],
split=True,
inner="quartile",
hue=split_feature,
palette=palette,
cut=0,
scale_hue=False,
)
fix_quartile_lines(ax)
ax.set_xlabel("")
ax.set_ylabel(numeric_feature, fontweight="bold")
l = ax.legend(loc=9)
if split_feature == "Gender":
l.get_texts()[0].set_text("Women")
l.get_texts()[1].set_text("Men")
plt.setp(ax.collections, alpha=0.5)
def downsample_auc(clf_cv, X, y, sample_weight=None):
X_ = X.copy()
downsample_features = ["Binary"]
X_["Binary"] = X[
"Changes_in_smell_i_cannot_smell_at_all_/_smells_smell_less_strong_than_they_did_before"
]
for n in [10, 100, 1000]:
if n == 1000:
feature = "Original scale"
else:
feature = "%d-point scale" % n
X_[feature] = (X["Smell_during_illness"] * n).round() / n
downsample_features.append(feature)
downsample_aucs = get_tuple_feature_aucs(
clf_cv, X_[downsample_features], y, 1, sample_weight=sample_weight, nicify_=False
)
downsample_features.remove("Original scale")
ax = downsample_aucs.loc[downsample_features[::-1]].plot.barh()
ax.legend().remove()
ax.set_xlabel("ROC AUC")
ax.set_ylabel("")
@auto_save_fig
def all_rocs(clf, X, y, classes, class_sets, cv, ax=None):
if ax is None:
plt.figure(figsize=(6, 6))
ax = plt.gca()
aucs = pd.Series(dtype="float")
for class_set in class_sets:
aucs["+".join(class_set)] = roc(
clf,
X,
y,
classes["features"],
class_set,
cv,
weights=None,
concat=True,
with_name=True,
ax=ax
)
ax.legend(fontsize=9, loc=4)
return aucs
@auto_save_fig
def single_auc(single_feature_aucs, classes, figsize=(10, 6)):
ax = single_plot(single_feature_aucs, [0.5, 0.72], 10, classes, figsize=figsize)
x1 = single_feature_aucs.loc["Symptoms_sore_throat", "ROC AUC"]
x2 = single_feature_aucs.loc[
"Changes_in_smell_i_cannot_smell_at_all_/_smells_smell_less_strong_than_they_did_before",
"ROC AUC",
]
x3 = single_feature_aucs.loc["Smell_during_illness", "ROC AUC"]
ax.axvline(x3, color="k", linestyle="--", ymax=21 / 22)
ax.axvline(x2, color="k", linestyle="--", ymax=12 / 22)
ax.axvline(x1, color="k", linestyle="--", ymax=5.5 / 22)
ax.arrow(
x1,
7.5,
(x2 - x1),
0,
width=0.1,
head_width=0.2,
head_length=0.01,
length_includes_head=True,
color="r",
)
ax.arrow(
x2,
4.5,
(x3 - x2),
0,
width=0.1,
head_width=0.2,
head_length=0.01,
length_includes_head=True,
color="r",
)
ax.text((x1 + x2) / 2, 7.9, "Δ=%.2f" % (x2 - x1), ha="center", fontsize=10)
ax.text((x2 + x3) / 2, 4.9, "Δ=%.2f" % (x3 - x2), ha="center", fontsize=10)
@auto_save_fig
def single_auc_delta(single_feature_aucs, reference_value, classes, figsize=(10, 6)):
ax = single_plot(
single_feature_aucs - reference_value,
[0, 0.025],
10,
classes,
delta=True,
figsize=figsize,
)
ax.set_yticklabels(['+%s' % x.get_text() for x in ax.get_yticklabels()]);
@auto_save_fig
def contour_plots(df, s, feature_x, feature_y, clusters):
ax = diagnosis_joint_plots(
df, feature_x, feature_y, s["recovery-data"], s
)
ax[0].set_xlim(-100, 25)
ax[1].set_ylim(-30, 100)
ax[0].axhline(33.3, linestyle="--", color="k")
ax[0].axvline(-33.3, linestyle="--", color="k")
ax[1].axhline(33.3, linestyle="--", color="k")
ax[1].axvline(-33.3, linestyle="--", color="k")
coords = {
"Intact\nSmell": (-6, -18),
"Recovered\nSmell": (-40, 73),
"Persistent\nSmell Loss": (-40, -18),
}
for kind, members in clusters.items():
ax[0].text(
coords[kind][0],
coords[kind][1],
"%s\n(%.1f%%)" % (kind, 100*len(members & s["lab-covid"])/len(s["recovery-data"] & s["lab-covid"])),
ha="right",
fontweight="normal",
)
ax[1].text(
coords[kind][0],
coords[kind][1],
"%s\n(%.1f%%)" % ("\n", 100*len(members & s["non-covid"])/len(s["recovery-data"] & s["non-covid"])),
ha="right",
fontweight="normal",
)
ax[0].text(
11,
90,
"n=%d" % len(s["recovery-data"] & s["lab-covid"]),
ha="center",
fontweight="normal",
fontsize=10
)
ax[1].text(
11,
90,
"n=%d" % len(s["recovery-data"] & s["non-covid"]),
ha="center",
fontweight="normal",
fontsize=10
)
ax[0].set_aspect("equal")
ax[1].set_aspect("equal")
#plt.tight_layout()
@auto_save_fig
def cramer_vs(df, classes, ax=None):
if ax is None:
plt.figure(figsize=(2, 12))
ax = plt.gca()
discrete_features = [
feature
for feature in df
if classes["dtypes"].get(feature, None) in ["categorical", "binary"]
]
cramer_v = pd.DataFrame(index=discrete_features, dtype=float)
for feature in cramer_v.index:
(
z,
_,
_,
cramer_v.loc[feature, "V"],
cramer_v.loc[feature, "Odds Ratio"],
) = contingency(df, [feature, "COVID Status"], verbose=False)
# Contingency table lists C19+ first, so ORs are inverted
cramer_v.loc[:, "Odds Ratio"] = 1 / cramer_v.loc[:, "Odds Ratio"]
cramer_v = cramer_v.sort_values("V").dropna()
cramer_v.index = nicify(list(cramer_v.index))
cramer_v = cramer_v.drop(["GCCR v"])
cramer_v["V+"] = cramer_v["V"] * (cramer_v["Odds Ratio"]>1)
cramer_v["V-"] = cramer_v["V"] * (cramer_v["Odds Ratio"]<1)
cramer_v["V+"].plot.barh(ax=ax, color='r', label="C19+ OR>1")
cramer_v["V-"].plot.barh(ax=ax, color='b', label="C19+ OR<1")
ax.set_xlim(0, 1)
ax.set_xlabel("Cramer V")
ax.set_xlim(0, 0.25)
ax.legend(loc=4, fontsize=10)
def odds_ratios_cutoff(df, diagnoses, feature, binary=None, xlim=None, ax=None):
if ax is None:
plt.figure(figsize=(4, 10))
ax = plt.gca()
thresholds = [0.1, 0.2, 0.5, 1, 2, 3, 5, 10, 25, 50, 75, 90, 99]
odds_ratios = pd.Series(index=["Binary"] + thresholds)
if binary:
odds_ratios["Binary"] = odds_ratio(
df,
diagnoses['clean'],
binary,
)
df_ = df.copy()
name = nicify(feature)
for threshold in thresholds:
df_["VAS Cutoff"] = df[feature] <= threshold
try:
odds_ratios[threshold] = odds_ratio(df_, diagnoses['clean'], "VAS Cutoff")
except:
odds_ratios[threshold] = None
odds_ratios.plot.barh(ax=ax)
ax.set_ylabel("VAS Cutoff (%s)" % name)
ax.set_xlabel("Odds ratio for C19")
if xlim:
ax.set_xlim(*xlim)
@auto_save_fig
def plot_entropy(df, feature, binary=None):
covid_entropy = entropy(df["COVID Status"].value_counts(normalize=True), base=2)
if binary:
binary_entropy = entropy(df[binary].value_counts(normalize=True), base=2)
else:
binary_entropy = None
z = contingency(df, ["COVID Status", "Symptoms_changes_in_smell"], verbose=False)[0]
binary_mi = mutual_info(z) / covid_entropy
rounders = [0.1, 1, 2, 5, 10, 20, 50]
continuous_entropy = {}
continuous_mi = {}
for rounder in rounders:
x = (df[feature] / rounder).round()
continuous_entropy[rounder] = entropy(x.value_counts(normalize=True), base=2)
df["rounded"] = x
z = contingency(df, ["COVID Status", "rounded"], verbose=False)[0]
continuous_mi[rounder] = (mutual_info(z) / covid_entropy)
fig, ax = plt.subplots(2, 1, figsize=(7, 7))
ax[0].barh(
["Binary"] + ["10-point scale"] + ["100-point scale"],
[binary_entropy, continuous_entropy[10], continuous_entropy[1]],
)
ax[0].invert_yaxis()
ax[0].set_xlabel("Entropy (bits)")
ax[1].barh(
["Binary"] + ["10-point scale"] + ["100-point scale"],
[binary_mi, continuous_mi[10], continuous_mi[1]],
)
ax[1].invert_yaxis()
ax[1].set_xlabel("Relative Information")
#ax[1].set_xticks([0, 0.01, 0.02])
plt.tight_layout()
def old_summary(Xu, s):
# Violin Plots (3 panels, one for before, during, after) like GCCR001 and dots. Possibly also pp plot.
fig, axes = plt.subplots(2, 2, figsize=(7, 6))
X = Xu.loc[(s["lab-covid"] | s["non-covid"]) & s["recovery-data"]]
y = X.index.to_series().isin(s["lab-covid"])
for i, feature in enumerate(
["Smell_before_illness", "Smell_during_illness", "Smell_after_illness"]
):
plot_violin(X, y, feature, axes.flat[i])
pp_plot(X, y, "Smell_before_illness", ax=axes.flat[3])
pp_plot(X, y, "Smell_during_illness", ax=axes.flat[3])
pp_plot(X, y, "Smell_after_illness", ax=axes.flat[3])
axes.flat[3].legend(fontsize=7)
plt.tight_layout()
def chem_and_block(df, s):
r = s["lab-covid"] & s["recovery-data"] & s["resp-recovery"]
chem_block_categories = {
"None": s["no_chem_or_block"] & r,
"B": s["no_chem_but_block"] & r,
"C": s["chem_no_block"] & r,
"B&C": s["chem_and_block"] & r,
}
feature_hist(df, chem_block_categories, "Smell_recovery_illness", bw=1, title="")
plt.figure()
df = status_map(df, chem_block_categories, "chem-block")
sns.set_style("whitegrid")
only = (s["male"] | s["female"]) & (s["lab-covid"] | s["non-covid"])
ax = sns.violinplot(
x="chem-block",
y="Age",
data=df.loc[only],
split=True,
inner="quartile",
hue="Gender",
palette={"F": "darkorange", "M": "green"},
cut=0,
scale_hue=0,
)
fix_quartile_lines(ax)
ax.set_ylim(18, 80)
ax.set_ylabel(nicify(ax.get_ylabel()), fontweight="bold")
ax.set_xlabel("")
labels = {"None": "None", "B": "Blockage", "C": "Chemesthesis", "B&C": "Both"}
ax.set_xticklabels([labels[x.get_text()] for x in ax.get_xticklabels()])
l = ax.legend(loc=(1.02, 0.81))
l.get_texts()[0].set_text("Women")
l.get_texts()[1].set_text("Men")
@auto_save_fig
def plot_yougov(rs, ax=None):
if ax is None:
plt.figure(figsize=(7, 5))
ax = plt.gca()
rs_ = rs.copy()
rs_.index = -rs.index
rs_.plot(linestyle='-', marker="o", ax=ax)
ax.set_xticks(range(-35, 36, 7))
ax.set_xticklabels([int(x / 7) for x in ax.get_xticks()])
ax.set_xlim(-43, 43)
ax.set_xlabel("YouGov Response Week - GCCR Response Week")
ax.set_ylabel("Correlation in C19+%\nbetween GCCR and YouGov")
def nmf_cluster(Xn, n_components, classes):
nmf = NMF(n_components, max_iter=10000)
X_cs = Xn[classes["features"]["chemosensory"]]
X_cs = X_cs.drop(
[x for x in X_cs if "after_ill" in x or "before_ill" in x or "during_ill" in x],
axis=1,
)
nmf.fit(X_cs)
nmf_weights = pd.DataFrame(nmf.components_.T, index=X_cs.columns).round(1)
s = sns.clustermap(
nmf_weights,
cmap="Greens",
xticklabels=range(1, nmf.n_components + 1),
yticklabels=nicify(list(nmf_weights.index)),
col_cluster=False,
figsize=(4, 7),
annot=True,
annot_kws={"fontsize": 9},
)
s.cax.remove()
plt.xlabel("NMF Component")
def draw_ontology(df, classes):
pos = {}
node_colors = {}
colors = 'rgbym'
for key, values in classes.items():
if 'CDC' in key:
continue
i = list(classes).index(key)
pos[key.upper()] = (0.5+i*0.5, 1)
node_colors[key.upper()] = colors[i]
for name in values:
name_ = nicify(name)
pos[name_] = (0.5+i*0.5, -1*(1+classes[key].index(name)))
node_colors[name_] = colors[i]
g = nx.Graph({key.upper(): nicify(value) for key, value in classes.items() if 'CDC' not in key})
plt.figure(figsize=(20, 20))
nx.draw(g, pos=pos, with_labels=True, font_size=15, node_shape="s",
node_color=list(node_colors.values()), bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.2'))
plt.xlim(0, 3);
@auto_save_fig
def age_and_gender(df, s, clusters):
fig, ax = plt.subplots(1, 2, sharey=True, figsize=(10, 4))
for i in range(2):
if i == 0:
only = s["lab-covid"] & (s["male"] | s["female"])
elif i == 1:
only = s["non-covid"] & (s["male"] | s["female"])
df = status_map(df, clusters, "status")
sns.violinplot(
x="status",
y="Age",
data=df.loc[only].sort_values("Gender"),
split=True,
inner="quartile",
order=list(clusters),
hue="Gender",
palette={"F": "darkorange", "M": "green"},
scale_hue=False,
cut=0,
ax=ax[i],
)
fix_quartile_lines(ax[i])
ax[i].set_title("COVID %s" % ("+" if i == 0 else "-"), fontweight="bold")
if i == 1:
l = ax[i].legend(loc=[0.3, 0.8])
l.get_texts()[0].set_text("Women")
l.get_texts()[1].set_text("Men")
else:
ax[i].legend().remove()
ax[i].set_xlabel("")
plt.setp(ax[i].collections, alpha=0.5)
ax[0].set_ylabel("Age", fontweight="bold")
ax[1].set_ylabel("")
ax[0].set_ylim(18, 85)
def predict_young_old(df, clf, X, y, s):
clf.fit(X, y)
thick_features = (
rank_coefs(clf, X, nicify_=False).head(10).index.drop("Intercept", errors="ignore")
)
ages = df["Age"].quantile(np.arange(0.05, 1, 0.05)).values
age_roc_auc = pd.DataFrame(index=ages)
for age in tqdm(ages):
s["old"] = get_set(df, "Age>%f" % age)
s["young"] = get_set(df, "Age<=%f" % age)
assert not (s["old"] & s["young"])
X_old = X.loc[s["old"] & (s["lab-covid"] | s["non-covid"])]
X_young = X.loc[s["young"] & (s["lab-covid"] | s["non-covid"])]
y_old = X_old.index.to_series().isin(s["lab-covid"])
y_young = X_young.index.to_series().isin(s["lab-covid"])
clf.fit(X_old[thick_features], y_old)
auc = roc_auc_score(y_young, clf.predict_proba(X_young[thick_features])[:, 1])
age_roc_auc.loc[age, "TestYoung"] = auc
age_roc_auc.loc[age, "YoungC19+"] = len(X_young.index.intersection(s["lab-covid"]))
age_roc_auc.loc[age, "YoungC19-"] = len(X_young.index.intersection(s["non-covid"]))
clf.fit(X_young[thick_features], y_young)
auc = roc_auc_score(y_old, clf.predict_proba(X_old[thick_features])[:, 1])
age_roc_auc.loc[age, "TestOld"] = auc
age_roc_auc.loc[age, "OldC19+"] = len(X_old.index.intersection(s["lab-covid"]))
age_roc_auc.loc[age, "OldC19-"] = len(X_old.index.intersection(s["non-covid"]))
age_roc_auc[["TestYoung", "TestOld"]].plot()
plt.xlabel("Age")
plt.ylabel("ROC AUC")
plt.legend()
def plot_double_features(double_features):
z = double_feature_aucs.copy()
z.index = pd.MultiIndex.from_tuples(list(double_feature_aucs.index.map(lambda x: x.split(' + '))))
z = z['ROC AUC'].unstack()
z = z.fillna(0) + z.T.fillna(0)
z.values[np.diag_indices_from(z)] /= 2
plt.figure(figsize=(20, 15))
sns.heatmap(z, xticklabels=z.index, yticklabels=z.index, cmap='Reds',
vmin=0.5, vmax=0.72, cbar_kws={'label': 'ROC Area Under Curve'});
def fig_letters(ax, n, x=-0.15, y=1.02):
for i in range(n):
ax[i].text(
x,
y,
"ABCDEFGHIJK"[i],
transform=ax[i].transAxes,
fontweight="bold",
fontsize=18,
)
@auto_save_fig
def collider_bias():
# Make layout
fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(9.25, 8))
# Make data for panels A and B
beta_samples = np.random.beta(1, 3, size=(1000, 2))
df = pd.DataFrame(beta_samples, columns=['Likelihood of COVID', 'Likelihood of Smell Loss'])
# Panel A
df.plot.scatter(x='Likelihood of COVID', y='Likelihood of Smell Loss', ax=ax[0, 0], alpha=0.5, c='black')
ax[0, 0].set_xlim(0, 1)
ax[0, 0].set_ylim(0, 1)
ax[0, 0].set_title('r=0')
# Panel B
df_gccr = df[df.sum(axis=1)>0.5]
df.plot.scatter(x='Likelihood of COVID', y='Likelihood of Smell Loss', ax=ax[0, 1], alpha=0.1, c='red')
df_gccr.plot.scatter(x='Likelihood of COVID', y='Likelihood of Smell Loss', ax=ax[0, 1], alpha=0.5, c='black')
ax[0, 1].set_title('r=%.2g' % df_gccr.corr().iloc[0, 1])
# Make data for panels C and D
from scipy.stats import norm, beta
cov = [[1, 0.5], [0.5, 1]]
chol = np.linalg.cholesky(cov)
corr_normal_samples = (chol @ np.random.randn(2, 1000)).T
corr_beta_samples = beta.ppf(norm.cdf(corr_normal_samples), 1, 3)
df2 = pd.DataFrame(corr_beta_samples, columns=['Likelihood of COVID', 'Likelihood of Smell Loss'])
# Panel C
df2.plot.scatter(x='Likelihood of COVID', y='Likelihood of Smell Loss', ax=ax[1, 0], alpha=0.5, c='black')
ax[1, 0].set_xlim(0, 1)
ax[1, 0].set_ylim(0, 1)
ax[1, 0].set_title('r=%.2g'% df2.corr().iloc[0, 1])
# Panel B
df2_gccr = df2[df2.sum(axis=1)>0.5]
df2.plot.scatter(x='Likelihood of COVID', y='Likelihood of Smell Loss', ax=ax[1, 1], alpha=0.1, c='red')
df2_gccr.plot.scatter(x='Likelihood of COVID', y='Likelihood of Smell Loss', ax=ax[1, 1], alpha=0.5, c='black')
ax[1, 1].set_title('r=%.2g' % df2_gccr.corr().iloc[0, 1])
# Label axes
for i in range(2):
ax[i, 0].set_ylabel('Likelihood of Smell Loss')
ax[1, i].set_xlabel('Likelihood of COVID')
# Add letters
fig_letters(ax.flat, 4, x=-0.25)
# Tidy up
plt.tight_layout()
@auto_save_fig
def odds_figure(Xu, y, feature):
X_ = Xu.loc[y.index, feature]
target_quantiles = X_.quantile(np.arange(0.005, 1, 0.01))
cutoff_quantiles = X_.quantile(np.arange(0, 1, 0.01))
z = cutoff_quantiles.searchsorted(X_)
X_rounded = target_quantiles.iloc[z].values
# Doing it the long way not the sns.lineplot way because
# we need to transform the error bars to odds space
sns.set_style("ticks")
fig, ax = plt.subplots(2, 1, sharex=True, figsize=(4, 8))
z = pd.DataFrame(np.vstack((X_rounded, y)).T, columns=["%s_rounded" % feature, "C19"])
p = z.groupby("%s_rounded" % feature).mean()["C19"]
n = z.groupby("%s_rounded" % feature).count()["C19"]
p_low = p - 2 * np.sqrt(p * (1 - p) / n)
p_high = p + 2 * np.sqrt(p * (1 - p) / n)
p_high = p_high.clip(0, 1)
ax[0].fill_between(p.index, p_low.values, p_high.values, color="gray", alpha=0.3)
ax[0].plot(p.index, p.values, c="black")
ax[0].set_xlim(0, 100)
ax[0].set_ylim(0, 1)
ax[0].axhline(0.5, linestyle="--", color="black", alpha=0.5)
ax[0].set_xlabel(nicify(feature))
ax[0].set_ylabel("p(C19+)")
odds = p / (1 - p)
odds_low = p_low / (1 - p_low)
odds_high = p_high / (1 - p_high)
ax[1].fill_between(
odds.index, odds_low.values, odds_high.values, color="lightblue", alpha=0.3
)
ax[1].plot(odds.index, odds.values, c="blue")
ax[1].set_ylim(0.1, 100)
ax[1].axhline(1, linestyle="--", color="black", alpha=0.5)
ax[1].set_yscale("log")
ax[1].yaxis.set_major_formatter(FormatStrFormatter("%.3g"))
ax[1].set_xlabel(nicify(feature))
ax[1].set_ylabel("odds(C19+)")
fig_letters(ax, 2, x=-0.25, y=1.05)
@auto_save_fig
def odds_cartoon(change_point=3, constant=1.2, slope=-0.33):
x = np.linspace(0, change_point, 100)
steady_state = 10 ** (slope * change_point + constant)
plt.figure(figsize=(6, 4))
ax = plt.gca()
ax.plot(x, 10 ** (slope * x + constant), color="blue")
ax.plot(x, 10 ** (slope * x + constant) / steady_state, color="brown")
x = np.linspace(change_point, 10, 100)
ax.plot(x, np.ones(100) * steady_state, linestyle="-", color="blue", label='odds (C19+)')
ax.plot(x, np.ones(100) * 1, linestyle="-", color="brown", label='odds ratio (C19+)')
ax.axhline(1, linestyle="--", color="black", alpha=0.5)
ax.set_xlim(0, 10)
ax.set_xlabel('ODoR-19 Scale')
ax.set_yscale("log")
ax.set_ylim(0.1, 100)
ax.yaxis.set_major_formatter(FormatStrFormatter("%.3g"))
ax.legend()
#ax[2].set_ylabel("odds(C19+)")
plt.tight_layout()
def plot_clipped_auc(clfcv, Xu, y, feature, ns=np.logspace(-1, 2, 16)):
clipped_auc = pd.Series(index=np.logspace(-1, 2, 16), dtype="float")
for n in tqdm(clipped_auc.index):
X_clipped = Xu.loc[y.index, [feature]].clip(0, n)
clfcv.fit(X_clipped, y)
clipped_auc[n] = clfcv.scores_[True].mean()
clipped_auc.plot()
plt.xlabel("%s\ntruncated below this value" % nicify(feature))
plt.ylabel("ROC AUC")
@auto_save_fig
def feature_vs_days(df, s, restrict=None, features=["Smell_change_illness", "Smell_recovery_illness"]):
ax = plt.gca()
if len(features)>1:
twinax = ax.twinx()
for i, feature in enumerate(features):
sns.lineplot(
x="Days_since_onset",
y=feature,
data=df if restrict is None else df.loc[restrict],
ax=ax if i==0 else twinax,
color='blue' if i==0 else 'darkgoldenrod',
)
ax.set_xlim(5, 40)
ax.set_ylim(-100, -45)
twinax.set_ylim(0, 55)
#ax.set_yticklabels([x / 2 for x in ax[0].get_yticks()])
# ax[0, 1].set_ylim(0, 2)
# ax[0].set_title("C19+")
# ax[0, 1].set_title("C19-")
ax.set_xlabel("Days Since Onset")
# ax[3, 1].set_xlabel("Days Since Onset")
ax.set_ylabel(nicify(features[0]), color='blue')
if len(features)>1:
twinax.set_ylabel(nicify(features[1]), color='darkgoldenrod')
plt.tight_layout()
#fig_letters(ax, 4, x=-0.25)
def which_cluster(df, classes, s, clusters):
discrete_features = [
feature
for feature in df
if classes["dtypes"].get(feature, None) in ["categorical", "binary"]
]
fig, ax = plt.subplots(1, 2, sharex=True, sharey=False, figsize=(5, 10))
three_clusters_pos = {
key: clusters[key] & s["lab-covid"] for key in clusters
}
two_clusters_pos = {
key: value
for key, value in three_clusters_pos.items()
if key in ["Persistent\nSmell Loss", "Recovered\nSmell"]
}
for i, cp in enumerate([three_clusters_pos, two_clusters_pos]):
df = status_map(df, cp, "Smell Recovery Status")
cramer_v = pd.Series(index=discrete_features, dtype=float)
for feature in cramer_v.index:
z, _, _, cramer_v[feature], _ = contingency(
df, [feature, "Smell Recovery Status"], verbose=False
)
cramer_v.index = nicify(list(cramer_v.index))