def plot_metrics_comparison_lineplot_grid(dataframe,
models_labels,
metrics_labels,
figure_size=(14, 4)):
"""
We define a function to plot the grid.
"""
return (
# Define the plot.
p9.ggplot(
dataframe,
p9.aes(x='threshold',
y='value',
group='variable',
color='variable',
shape='variable'))
# Add the points and lines.
+ p9.geom_point() + p9.geom_line()
# Rename the x axis and give some space to left and right.
+ p9.scale_x_discrete(name='Threshold', expand=(0, 0.2))
# Rename the y axis, give some space on top and bottom, and print the tick labels with 2 decimal digits.
+
p9.scale_y_continuous(name='Value',
expand=(0, 0.05),
labels=lambda l: ['{:.2f}'.format(x) for x in l])
# Replace the names in the legend.
+ p9.scale_shape_discrete(
name='Metric', labels=lambda l: [metrics_labels[x] for x in l])
# Define the colors for the metrics for color-blind people.
+
p9.scale_color_brewer(name='Metric',
labels=lambda l: [metrics_labels[x] for x in l],
type='qual',
palette='Set2')
# Place the plots in a grid, renaming the labels for rows and columns.
+ p9.facet_grid('iterations ~ model',
labeller=p9.labeller(
rows=lambda x: f'iters = {x}',
cols=lambda x: f'{models_labels[x]}'))
# Define the theme for the plot.
+ p9.theme(
# Remove the y axis name.
axis_title_y=p9.element_blank(),
# Set the size of x and y tick labels font.
axis_text_x=p9.element_text(size=7),
axis_text_y=p9.element_text(size=7),
# Place the legend on top, without title, and reduce the margin.
legend_title=p9.element_blank(),
legend_position='top',
legend_box_margin=2,
# Set the size for the figure.
figure_size=figure_size,
))
def plot_preprocessing_boxplot_bymodel(dataframe,
models_labels,
metrics_labels,
groups_labels,
figure_size=(14, 4)):
"""
We define a function to plot the grid.
"""
return (
# Define the plot.
p9.ggplot(dataframe, p9.aes(x='variable', y='value', fill='group'))
# Add the boxplots.
+ p9.geom_boxplot(position='dodge')
# Rename the x axis.
+ p9.scale_x_discrete(name='Metric',
labels=lambda l: [metrics_labels[x] for x in l])
# Rename the y axis.
+ p9.scale_y_continuous(
name='Value',
expand=(0, 0.05),
# breaks=[-0.25, 0, 0.25, 0.5, 0.75, 1], limits=[-0.25, 1],
labels=lambda l: ['{:.2f}'.format(x) for x in l])
# Define the colors for the metrics for color-blind people.
+ p9.scale_fill_brewer(name='Group',
labels=lambda l: [groups_labels[x] for x in l],
type='qual',
palette='Set2')
# Place the plots in a grid, renaming the labels.
+ p9.facet_grid(
'model ~ .',
scales='free_y',
labeller=p9.labeller(rows=lambda x: f'{models_labels[x]}'))
# Define the theme for the plot.
+ p9.theme(
# Remove the x and y axis names.
axis_title_x=p9.element_blank(),
axis_title_y=p9.element_blank(),
# Set the size of x and y tick labels font.
axis_text_x=p9.element_text(size=7),
axis_text_y=p9.element_text(size=7),
# Place the legend on top, without title, and reduce the margin.
legend_title=p9.element_blank(),
legend_position='top',
legend_box_margin=2,
# Set the size for the figure.
figure_size=figure_size,
))
def plot_distributions_bar_plot_grid(dataframe, figure_size=(14, 4)):
"""
We create a function to plot the bar plot.
"""
return (
# Define the plot.
p9.ggplot(dataframe, p9.aes(x='threshold', fill='value'))
# Add the bars.
+ p9.geom_bar(position='dodge') +
p9.geom_text(p9.aes(label='stat(count)'),
stat='count',
position=p9.position_dodge(0.9),
size=7,
va='bottom')
# Rename the x axis.
+ p9.scale_x_discrete(name='Threshold')
# Rename the y axis, give some space on top and bottom (mul_bottom, add_bottom, mul_top, add_top).
+ p9.scale_y_continuous(name='Count', expand=(0, 0, 0, 500))
# Replace the names in the legend and set the colors of the bars.
+ p9.scale_fill_manual(values={
0: '#009e73',
1: '#d55e00'
},
labels=lambda l: [{
0: 'Stable',
1: 'Unstable'
}[x] for x in l])
# Place the plots in a grid, renaming the labels.
+ p9.facet_grid('. ~ iterations',
labeller=p9.labeller(cols=lambda x: f'iters = {x}'))
# Define the theme for the plot.
+ p9.theme(
# Remove the y axis name.
axis_title_y=p9.element_blank(),
# Set the size of x and y tick labels font.
axis_text_x=p9.element_text(size=7),
axis_text_y=p9.element_text(size=7),
# Place the legend on top, without title, and reduce the margin.
legend_title=p9.element_blank(),
legend_position='top',
legend_box_margin=2,
# Set the size for the figure.
figure_size=figure_size,
))
def plot_xbs(df, group, var, n_side=9, n_delta=6):
r"""Construct Xbar and S chart
Construct an Xbar and S chart to assess the state of statistical control of
a dataset.
Args:
df (DataFrame): Data to analyze
group (str): Variable for grouping
var (str): Variable to study
Keyword args:
n_side (int): Number of consecutive runs above/below centerline to flag
n_delta (int): Number of consecutive runs increasing/decreasing to flag
Returns:
plotnine object: Xbar and S chart
Examples::
import grama as gr
DF = gr.Intention()
from grama.data import df_shewhart
(
df_shewhart
>> gr.tf_mutate(idx=DF.index // 10)
>> gr.pt_xbs("idx", "tensile_strength")
)
"""
## Prepare the data
DF = Intention()
df_batched = (df >> tf_group_by(group) >> tf_summarize(
X=mean(DF[var]),
S=sd(DF[var]),
n=nfcn(DF.index),
) >> tf_ungroup())
df_stats = (df_batched >> tf_summarize(
X_center=mean(DF.X),
S_biased=mean(DF.S),
n=mean(DF.n),
))
n = df_stats.n[0]
df_stats["S_center"] = df_stats.S_biased / c_sd(n)
df_stats["X_LCL"] = df_stats.X_center - 3 * df_stats.S_center / sqrt(n)
df_stats["X_UCL"] = df_stats.X_center + 3 * df_stats.S_center / sqrt(n)
df_stats["S_LCL"] = B3(n) * df_stats.S_center
df_stats["S_UCL"] = B4(n) * df_stats.S_center
## Reshape for plotting
df_stats_long = (df_stats >> tf_pivot_longer(
columns=["X_LCL", "X_center", "X_UCL", "S_LCL", "S_center", "S_UCL"],
names_to=["_var", "_stat"],
names_sep="_",
values_to="_value",
))
# Fake group value to avoid issue with discrete group variable
df_stats_long[group] = [df_batched[group].values[0]
] * df_stats_long.shape[0]
df_batched_long = (
df_batched >> tf_pivot_longer(
columns=["X", "S"],
names_to="_var",
values_to="_value",
)
## Flag patterns
>> tf_left_join(
df_stats >> tf_pivot_longer(
columns=[
"X_LCL", "X_center", "X_UCL", "S_LCL", "S_center", "S_UCL"
],
names_to=["_var", ".value"],
names_sep="_",
),
by="_var",
) >> tf_group_by("_var") >> tf_mutate(
outlier_below=(DF._value < DF.LCL), # Outside control limits
outlier_above=(DF.UCL < DF._value),
below=consec(DF._value < DF.center, i=n_side), # Below mean
above=consec(DF.center < DF._value, i=n_side), # Above mean
) >> tf_mutate(
decreasing=consec((lead(DF._value) - DF._value) < 0, i=n_delta - 1)
| # Decreasing
consec((DF._value - lag(DF._value)) < 0, i=n_delta - 1),
increasing=consec(0 < (lead(DF._value) - DF._value), i=n_delta - 1)
| # Increasing
consec(0 < (DF._value - lag(DF._value)), i=n_delta - 1),
) >> tf_mutate(
sign=case_when([DF.outlier_below, "-2"], [DF.outlier_above, "+2"],
[DF.below | DF.decreasing, "-1"],
[DF.above | DF.increasing, "+1"], [True, "0"]),
glyph=case_when(
[DF.outlier_below, "Below Limit"],
[DF.outlier_above, "Above Limit"],
[DF.below, "Low Run"],
[DF.above, "High Run"],
[DF.increasing, "Increasing Run"],
[DF.decreasing, "Decreasing Run"],
[True, "None"],
)) >> tf_ungroup())
## Visualize
return (df_batched_long >> ggplot(aes(x=group)) + geom_hline(
data=df_stats_long,
mapping=aes(yintercept="_value", linetype="_stat"),
) + geom_line(aes(y="_value", group="_var"), size=0.2) + geom_point(
aes(y="_value", color="sign", shape="glyph"),
size=3,
) + scale_color_manual(values={
"-2": "blue",
"-1": "darkturquoise",
"0": "black",
"+1": "salmon",
"+2": "red"
}, ) + scale_shape_manual(
name="Patterns",
values={
"Below Limit": "s",
"Above Limit": "s",
"Low Run": "X",
"High Run": "X",
"Increasing Run": "^",
"Decreasing Run": "v",
"None": "."
},
) + scale_linetype_manual(
name="Guideline",
values=dict(LCL="dashed", UCL="dashed", center="solid"),
) + guides(color=None) + facet_grid(
"_var~.",
scales="free_y",
labeller=labeller(dict(X="Mean", S="Variability")),
) + labs(
x="Group variable ({})".format(group),
y="Value ({})".format(var),
))
from plotnine.data import mtcars
def number_to_word(n):
lst = [
'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight',
'nine'
]
try:
return lst[int(n)]
except IndexError:
return str(n)
labeller_cols_both = labeller(rows='label_value',
cols='label_both',
multi_line=False)
labeller_towords = labeller(rows='label_both',
cols='label_both',
multi_line=False,
am=number_to_word,
gear=number_to_word)
g = ggplot(mtcars, aes(x='wt', y='mpg')) + geom_point()
def test_label_value():
p = g + facet_wrap('~ gear', labeller='label_value')
assert p == 'label_value'
p9.facet_grid("lambda_d ~ lambda_c", labeller="label_both", scales="free")
+ p9.geom_point() + p9.theme(figure_size=(12, 12)))
print(g)
lambda_c_grid = np.linspace(1e-6, 1e-3, num=5)
lambda_d_grid = np.linspace(1e-6, 1e-3, num=5)
plot_df = run_saucie_param_grid(full_paper_dataset_subset,
lambda_c_grid=lambda_c_grid,
lambda_d_grid=lambda_d_grid)
g = (p9.ggplot(plot_df) + p9.aes(x="dim1", y="dim2", fill="journal") +
p9.facet_grid(
"lambda_d ~ lambda_c",
labeller=p9.labeller(
cols=lambda s: f"lambda_c: {float(s):.3e}",
rows=lambda s: f"lambda_d: {float(s):.3e}",
),
scales="free",
) + p9.geom_point() + p9.theme(figure_size=(12, 12)))
g.save("output/figures/saucie_hyperparam_lambda_cd.png", dpi=500)
print(g)
learning_rate_grid = np.linspace(1e-6, 1e-3, num=3)
steps_grid = [1000, 3000, 5000, 10000, 10000]
plot_df = run_saucie_param_grid(
full_paper_dataset_subset,
lambda_c_grid=[1.000e-3],
lambda_d_grid=[1.000e-3],
steps_grid=steps_grid,
learning_rate_grid=learning_rate_grid,
from plotnine import ggplot, aes, geom_point
from plotnine import facet_grid, facet_wrap
from plotnine import labeller, as_labeller
from plotnine.data import mtcars
def number_to_word(n):
lst = ['zero', 'one', 'two', 'three', 'four',
'five', 'six', 'seven', 'eight', 'nine']
try:
return lst[int(n)]
except IndexError:
return str(n)
labeller_cols_both = labeller(rows='label_value', cols='label_both',
multi_line=False)
labeller_towords = labeller(rows='label_both', cols='label_both',
multi_line=False, am=number_to_word,
gear=number_to_word)
g = ggplot(mtcars, aes(x='wt', y='mpg')) + geom_point()
def test_label_value():
p = g + facet_wrap('~ gear', labeller='label_value')
assert p == 'label_value'
def test_label_both():
