def plot_hist_fit_auto(df, branch, cut_BDT=None, **kwargs):
""" Retrieve the latex name of the branch and unit. Set the folder name to the name of the datasets.
Then, plot 2d histogram with plot_hist_fit.
Parameters
----------
df : pandas.Dataframe
dataframe that contains the branch to plot
branch : str
branch (for instance: ``'B0_M'``), in dataframe
cut_BDT : float or str
``BDT > cut_BDT`` cut. Used in the name of saved figure.
**kwargs : dict
arguments passed in :py:func:`plot_hist_fit` (except ``branch``, ``latex_branch``, ``unit``)
Returns
-------
fig : matplotlib.figure.Figure
Figure of the plot (only if ``axis_mode`` is ``False``)
ax[0] : matplotlib.figure.Axes
Axis of the histogram + fitted curves + table
ax[1] : matplotlib.figure.Axes
Axis of the pull diagram (only if ``plot_pull`` is ``True``)
"""
# Retrieve particle name, and branch name and unit.
# particle, var = retrieve_particle_branch(branch)
# latex_branch = branchs_params[var]['name']
# unit = branchs_params[var]['unit']
# name_particle = particle_names[particle]
latex_branch, unit = pt.get_latex_branches_units(branch)
# Title and name of the file with BDT
add_in_dic('fig_name', kwargs)
add_in_dic('title', kwargs)
add_in_dic('data_name', kwargs)
kwargs['fig_name'] = pt._get_fig_name_given_BDT_cut(
fig_name=kwargs['fig_name'],
cut_BDT=cut_BDT,
branch=branch,
data_name=string.add_text(kwargs['data_name'], 'fit', '_', None))
kwargs['title'] = pt._get_title_given_BDT_cut(title=kwargs['title'],
cut_BDT=cut_BDT)
# Name of the folder = name of the data
add_in_dic('folder_name', kwargs)
if kwargs['folder_name'] is None and kwargs['data_name'] is not None:
kwargs['folder_name'] = kwargs['data_name']
return plot_hist_fit(df,
branch,
latex_branch=latex_branch,
unit=unit,
**kwargs)
def _get_title_given_BDT_cut(title, cut_BDT):
""" Return the the new title given the cut on the BDT
Parameters
----------
title : str
initial title
cut_BDT : float
cut on the BDT (we keep ``BDT > {cut_BDT}``)
Returns
-------
title: str
new title
Examples
--------
>>> _get_title_given_BDT_cut("title", -0.1)
"title - BDT > -0.1"
"""
# Title with BDT
if cut_BDT is not None:
title = string.add_text(title, f"BDT $>$ {cut_BDT}", ' - ')
return title
def get_raw_branch(particle, raw_variable):
"""
Parameters
----------
particle: str or tuple(str) or None
particle or list of particles
raw_variable: str
name of the raw variable
Returns
-------
raw_branch: str or tuple(str)
Name of the raw branch or tuple of the names of the raw branches
* ``{raw_quantity}`` if there is one raw quantity, zero particle
* ``raw_variable = {particle}_{raw_quantity}`` if there is one raw quantity, one particle
* Tuple of ``tuple({raw_variable[0]},{raw_variable[1]})`` if there is two raw variables
* ...
"""
if assertion.is_list_tuple(raw_variable):
particle = tuple(el_to_list(particle, len(raw_variable)))
return tuple(RVariable.get_raw_branch(sub_particle, sub_raw_quantity)
for sub_particle, sub_raw_quantity in zip(particle, raw_variable))
return add_text(particle, raw_variable)
def apply_BDT(df_tot,
df_train,
bdt,
BDT_name=None,
save_BDT=False,
kind_data='common'):
"""
* Apply the BDT to the dataframe ``df_train`` which contains only the training variable.
* Add the BDT output as a new variable in ``df_tot``.
* Save ``df_tot`` in a root file ``{loc['root']}/{kind_data}_{ BDT_name}.root`` (branch ``'DecayTree'``)
* In addition, save the BDT output in a separated root file ``{loc['root']t/BDT_{BDT_name}.root`` (branch ``'BDT'``)
* if ``save_BDT`` is ``True``, save the BDT in a root file ``{loc['pickle']}/bdt_{BDT_name}.pickle``
Parameters
----------
df_tot : pandas.Dataframe
dataframe that will be saved together with the BDT output
df_train : pandas.Dataframe
dataframe with only the variables that have been used for the training
bdt : sklearn.ensemble.AdaBoostClassifier or sklearn.ensemble.GradientBoostingClassifier
trained BDT classifier
BDT_name : str
name of the BDT, used for the name of the saved files
save_BDT : bool
if ``True``, save the BDT in a pickle file
kind_data : str
name of the data where the BDT is applied to (e.g., ``'MC'``, ``'common'``, ...)
"""
# Apply the BDT to the dataframe that contains only the variables used in
# the training, in the right order
df_tot['BDT'] = bdt.decision_function(df_train)
file_name = string.add_text(kind_data, BDT_name, '_')
df = pd.DataFrame()
df['BDT'] = df_tot['BDT']
save_root(df, 'BDT_' + file_name, 'DecayTree')
save_root(df_tot, file_name, 'DecayTree')
if save_BDT:
dump_pickle(bdt, string.add_text('bdt', file_name, '_'))
def _get_fig_name_given_BDT_cut(fig_name=None,
cut_BDT=None,
branch="",
data_name=None):
""" Return the new name of the file and the new title given the cut on the BDT
Parameters
----------
fig_name : str
initial name of the file
cut_BDT : float
cut on the BDT (we keep ``BDT > {cut_BDT}``)
branch : float
a name of branch (e.g., ``'B0_M'``)
data_name : str or None
name of the plotted data
Returns
-------
fig_name: str
new fig_name
Examples
--------
>>> _get_title_given_BDT_cut("fig_name", -0.1, 'B0_M', 'MC')
"fig_BDT_name-0.1"
>>> _get_title_given_BDT_cut(None, -0.1, 'B0_M', 'MC')
"B0_M_MC_BDT-0.1"
"""
assert (fig_name is not None) or (data_name is not None)
if fig_name is None:
fig_name = string.add_text(branch, data_name, '_')
# Title with BDT
if cut_BDT is not None:
fig_name = string.add_text(fig_name, f'BDT{cut_BDT}')
return fig_name
def quantity(self):
""" Combined physical quantity, with the function applied to it. Only defined if there is zero or one particle.
* ``{raw_quantity}`` if there is one raw branch and no function
* ``{raw_quantity}:{name_function}`` if there is one raw quantity and a function
* ``{raw_quantity[0]},{raw_quantity[1]}:{name_function}`` if there are two raw variables and a function
"""
if assertion.is_list_tuple(self.particle) and len(self.particle) > 1:
return None
else:
if isinstance(self.raw_quantity, str):
raw_quantities = [self.raw_quantity]
else:
raw_quantities = self.raw_quantity
return add_text(','.join(raw_quantities),
self.name_function, sep=':')
def classification_report_print(X_test, y_test, bdt, BDT_name=None):
""" Test the bdt training with the testing sample.\
Print and save the report in ``{loc['tables']}/BDT/{BDT_name}/classification_report.txt``.
Parameters
----------
X_text : numpy.ndarray
Array with signal and MC data concatenated and shuffled for test
y_test : numpy.array
Array with 1 for the signal events, and 0 for background events (shuffled) for test
bdt : sklearn.ensemble.AdaBoostClassifier or sklearn.ensemble.GradientBoostingClassifier
trained classifier
BDT_name : str
name of the BDT, used for the path of the saved txt file.
"""
# if xgboost:
# y_predicted = xgbmodel.predict_proba(X)[:,1]
# else:
y_predicted = bdt.predict(X_test)
classification_report_str = classification_report(
y_test, y_predicted, target_names=["background", "signal"])
print(classification_report_str)
ROC_AUC_score = roc_auc_score(
y_test, # real
bdt.decision_function(X_test))
# bdt.decision_function(X_test) = scores = returns a Numpy array, in which each element
# represents whether a predicted sample for x_test by the classifier lies to the right
# or left side of the Hyperplane and also how far from the HyperPlane.
print("Area under ROC curve: %.4f" % (ROC_AUC_score))
# Write the results -----
fig_name = string.add_text('classification_report', BDT_name, '_')
path = create_directory(f"{loc['tables']}/BDT/", BDT_name)
with open(f"{path}/{fig_name}.txt", 'w') as f:
f.write(classification_report_str)
f.write("Area under ROC curve: %.4f" % (ROC_AUC_score))
def plot_scatter2d(dfs,
branches,
latex_branches,
units=[None, None],
low=None,
high=None,
n_bins=100,
colors=['g', 'r', 'o', 'b'],
data_name=None,
title=None,
fig_name=None,
folder_name=None,
fontsize_label=default_fontsize['label'],
save_fig=True,
ax=None,
get_sc=False,
pos_text_LHC=None,
**params):
""" Plot a 2D histogram of 2 branches.
Parameters
----------
dfs : pandas.Dataframe or list(pandas.Dataframe)
Dataset or list of datasets.
branches : [str, str]
names of the two branches
latex_branches : [str, str]
latex names of the two branches
units : str or [str, str]
Common unit or list of two units of the two branches
n_bins : int or [int, int]
number of bins
log_scale : bool
if true, the colorbar is in logscale
low : float or [float, float]
low value(s) of the branches
high : float or [float, float]
high value(s) of the branches
data_name : str
name of the data, this is used to define the name of the figure,
in the case ``fig_name`` is not defined, and define the legend if there is more than 1 dataframe.
colors : str or list(str)
color(s) used for the histogram(s)
title : str
title of the figure
fig_name : str
name of the saved figure
folder_name : str
name of the folder where to save the figure
fontsize_label : float
fontsize of the label of the axes
save_fig : bool
specifies if the figure is saved
ax : matplotlib.axes.Axes
axis where to plot
get_sc : bool
if True: get the scatter plot
pos_text_LHC : dict, list or str
passed to :py:func:`HEA.plot.tools.set_text_LHCb` as the ``pos`` argument.
Returns
-------
fig : matplotlib.figure.Figure
Figure of the plot (only if ``ax`` is not specified)
ax : matplotlib.figure.Axes
Axis of the plot (only if ``ax`` is not specified)
scs : matplotlib.PathCollection or list(matplotlib.PathCollection)
scatter plot or list of scatter plots (only if ``get_sc`` is ``True``)
"""
# low, high and units into a list of size 2
low = el_to_list(low, 2)
high = el_to_list(high, 2)
units = el_to_list(units, 2)
if ax is not None:
save_fig = False
fig, ax = get_fig_ax(ax)
title = string.add_text(None, title, default=None)
ax.set_title(title, fontsize=25)
scs = [None] * len(dfs)
for k, (data_name, df) in enumerate(dfs.items()):
scs[k] = ax.scatter(df[branches[0]],
df[branches[1]],
c=colors[k],
label=data_name,
**params)
if len(scs) == 1:
scs = scs[0]
ax.set_xlim([low[0], high[0]])
ax.set_ylim([low[1], high[1]])
# Label, color bar
pt.set_label_ticks(ax)
pt.set_text_LHCb(ax, pos=pos_text_LHC)
set_label_2Dhist(ax, latex_branches, units, fontsize=fontsize_label)
# Save the data
if save_fig:
pt.save_fig(
fig, fig_name, folder_name,
string.add_text(string.list_into_string(branches, '_vs_'),
string.list_into_string(data_name, '_'), '_'))
if fig is not None:
if get_sc:
return fig, ax, scs
else:
return fig, ax
else:
if get_sc:
return scs
def plot_hist2d(df,
branches,
latex_branches,
units,
low=None,
high=None,
n_bins=100,
log_scale=False,
title=None,
fig_name=None,
folder_name=None,
data_name=None,
save_fig=True,
ax=None,
pos_text_LHC=None):
""" Plot a 2D histogram of 2 branches.
Parameters
----------
df : pandas.Dataframe
Dataframe that contains the 2 branches to plot
branches : [str, str]
names of the two branches
latex_branches : [str, str]
latex names of the two branches
units : str or [str, str]
Common unit or list of two units of the two branches
n_bins : int or [int, int]
number of bins
log_scale : bool
if true, the colorbar is in logscale
low : float or [float, float]
low value(s) of the branches
high : float or [float, float]
high value(s) of the branches
title : str
title of the figure
fig_name : str
name of the saved figure
folder_name : str
name of the folder where to save the figure
data_name : str
name of the data, this is used to define the name of the figure,
in the case ``fig_name`` is not defined.
save_fig : bool
specifies if the figure is saved
ax : matplotlib.axes.Axes
axis where to plot
pos_text_LHC : dict, list or str
passed to :py:func:`HEA.plot.tools.set_text_LHCb` as the ``pos`` argument.
Returns
-------
fig : matplotlib.figure.Figure
Figure of the plot (only if ``ax`` is not specified)
ax : matplotlib.figure.Axes
Axis of the plot (only if ``ax`` is not specified)
"""
# low, high and units into a list of size 2
low = el_to_list(low, 2)
high = el_to_list(high, 2)
units = el_to_list(units, 2)
for i in range(2):
low[i], high[i] = pt._redefine_low_high(low[i], high[i],
df[branches[i]])
# Plotting
fig, ax = get_fig_ax(ax)
title = string.add_text(data_name, title, default=None)
ax.set_title(title, fontsize=25)
if log_scale:
_, _, _, h = ax.hist2d(df[branches[0]],
df[branches[1]],
range=[[low[0], high[0]], [low[1], high[1]]],
bins=n_bins,
norm=LogNorm())
else:
_, _, _, h = ax.hist2d(df[branches[0]],
df[branches[1]],
range=[[low[0], high[0]], [low[1], high[1]]],
bins=n_bins)
# Label, color bar
pt.set_label_ticks(ax)
pt.set_text_LHCb(ax, pos=pos_text_LHC)
set_label_2Dhist(ax, latex_branches, units, fontsize=25)
cbar = plt.colorbar(h)
cbar.ax.tick_params(labelsize=20)
return end_plot_function(fig,
save_fig=save_fig,
fig_name=fig_name,
folder_name=folder_name,
default_fig_name=string.add_text(
string.list_into_string(branches, '_vs_'),
data_name, '_'),
ax=ax)
def _plot_single_model(ax,
x,
model,
plot_scaling,
model_type=None,
model_name=None,
frac=1.,
color='b',
linestyle='-',
line_width=2.5,
alpha=1):
""" Plot the models recursively
with a label for the curve ``"{name of the PDF (e.g., Gaussian, ...)} - {type of the model, e.g., signal ...} {Name of the model, e.g., "B0->Dst Ds"}"`` (if ``model_name`` is specified)
ax : matplotlib.axes.Axes
axis where to plot
x : numpy.numpy(float)
points of the x-axis where to evaluate the pdf of the model to plot
model : zfit.pdf.BasePDF
just one zfit model
plot_scaling : float
scaling to get the scale of the curve right
model_type : str
type of the model
* ``'m'`` : model (sum) ; should always be the FIRST ONE !!
* ``'s'`` : signal
* ``'b'`` : background
used in the legend to indicate if it is a signal or a background component
model_name : str
name of the models - used in the legend.
If ``None``, the legend is not shown
frac : float
frac is multiplied to the PDF to get the correct scale due to composite PDFs
color : str
list of colors for each curve, same structure as ``models_names``
linestyle : str
line style of the curve
PDF_level : int
Level of the PDF:
* 0 is first sumPDF
* 1 if component of this sumPDF
* 2 if component of a sumPDF component of sumPDF
* etc.
line_width : float
width of the plotted lines
"""
assert not assertion.is_list_tuple(model)
# Label
if model_name is not None:
label_model = f'{get_model_name(model)} - {model_names_types[model_type]}'
label_model = string.add_text(label_model, model_name)
else:
label_model = None
plot_fitted_curve(ax,
model,
plot_scaling,
frac=frac,
line_width=line_width,
color=color,
linestyle=linestyle,
label=label_model,
x=x,
alpha=alpha)
def correlations(data, fig_name=None, folder_name=None, title=None, **kwds):
""" Calculate pairwise correlation between features of the dataframe data
and save the figure in ``{loc['plot']}/BDT/{folder_name}/corr_matrix_{fig_name}``
Parameters
----------
data : pandas.Dataframe
dataset
fig_name : str
name of the saved file
folder_name : str
name of the folder where to save the plot
**kwds : dict
other plotting keyword arguments, to be passed to ``pandas.DataFrame.corr()``
Returns
-------
fig : matplotlib.figure.Figure
Figure of the plot
ax : matplotlib.figure.Axes
Axis of the plot
"""
# simply call df.corr() to get a table of
# correlation values if you do not need
# the fancy plotting
corrmat = data.corr(**kwds) # correlation
fig, ax1 = plt.subplots(ncols=1, figsize=(12, 10)) # 1 plot
opts = {
'cmap': plt.get_cmap("RdBu"), # red blue color mode
'vmin': -1,
'vmax': +1
} # correlation between -1 and 1
heatmap1 = ax1.pcolor(corrmat, **opts) # create a pseudo color plot
plt.colorbar(heatmap1, ax=ax1) # color bar
title = string.add_text("Correlations", title, ' - ')
ax1.set_title(title)
labels = list(corrmat.columns.values) # get the list of labels
for i, label in enumerate(labels):
latex_branch, _ = RVariable.get_latex_branch_unit_from_branch(label)
labels[i] = latex_branch
# shift location of ticks to center of the bins
ax1.set_xticks(np.arange(len(labels)) + 0.5, minor=False)
ax1.set_yticks(np.arange(len(labels)) + 0.5, minor=False)
ax1.set_xticklabels(labels, minor=False, ha='right', rotation=70)
ax1.set_yticklabels(labels, minor=False)
plt.tight_layout()
if fig_name is None:
fig_name = string.list_into_string(column)
pt.save_fig(fig,
f"corr_matrix_{fig_name}",
folder_name=f'BDT/{folder_name}')
return fig, ax1
