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Edited parity plot to allow color to represent 3rd dimension. Still need to improve colorbar axis name.

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pull/1/head
titusquah 5 years ago
parent 77e4cefd2c
commit 97ca231afc
4 changed files with 147 additions and 93 deletions
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2
.idea/dictionaries/Titus.xml
Unescape View File

@ -19,11 +19,13 @@
<w>ndarray</w> <w>ndarray</w>
<w>pred</w> <w>pred</w>
<w>quah</w> <w>quah</w>
<w>rbfopt</w>
<w>reeps</w> <w>reeps</w>
<w>scipy</w> <w>scipy</w>
<w>seaborn</w> <w>seaborn</w>
<w>slsqp</w> <w>slsqp</w>
<w>thermo</w> <w>thermo</w>
<w>viridis</w>
<w>xmls</w> <w>xmls</w>
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126
.idea/workspace.xml
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@ -4,13 +4,8 @@
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<screen x="0" y="0" width="1920" height="1040" /> <screen x="0" y="0" width="1920" height="1040" />
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<state x="579" y="298" key="com.intellij.ide.util.TipDialog/0.0.1920.1040@0.0.1920.1040" timestamp="1591971684146" /> <state x="263" y="182" width="1283" height="717" key="com.intellij.ide.util.TipDialog/0.0.1920.1040@0.0.1920.1040" timestamp="1592167059593" />
<state x="-1151" y="440" key="com.intellij.openapi.vcs.update.UpdateOrStatusOptionsDialogupdate-v2" timestamp="1591821870917"> <state x="-1151" y="440" key="com.intellij.openapi.vcs.update.UpdateOrStatusOptionsDialogupdate-v2" timestamp="1591821870917">
<screen x="-1920" y="2" width="1920" height="1040" /> <screen x="-1920" y="2" width="1920" height="1040" />
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@ -351,10 +355,10 @@
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<screen x="-1920" y="2" width="1920" height="1040" /> <screen x="-1920" y="2" width="1920" height="1040" />
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106
reeps/reeps.py
Unescape View File

@ -64,7 +64,8 @@ class REEPS:
The ordering of the columns needs to be: The ordering of the columns needs to be:
[h_i, h_eq, z_i, z_eq, {RE_1}_aq_i, {RE_1}_aq_eq, {RE_1}_d_eq, [h_i, h_eq, z_i, z_eq,
{RE_1}_aq_i, {RE_1}_aq_eq, {RE_1}_d_eq,
{RE_2}_aq_i, {RE_2}_aq_eq, {RE_2}_d_eq,... {RE_2}_aq_i, {RE_2}_aq_eq, {RE_2}_d_eq,...
{RE_N}_aq_i, {RE_N}_aq_eq, {RE_N}_d_eq] {RE_N}_aq_i, {RE_N}_aq_eq, {RE_N}_d_eq]
@ -347,15 +348,17 @@ class REEPS:
self.update_predicted_dict() self.update_predicted_dict()
@staticmethod @staticmethod
def slsqp_optimizer(objective, x_guess): def scipy_minimize(objective, x_guess, optimizer_kwargs=None):
""" The default optimizer for REEPS """ The default optimizer for REEPS
Uses scipy.minimize with options Uses scipy.minimize
By default, options are
.. code-block:: python .. code-block:: python
default_kwargs= {"method": 'SLSQP', default_kwargs= {"method": 'SLSQP',
"bounds": [(1e-1, 1e1)*len(x_guess)], "bounds": [(1e-1, 1e1)]*len(x_guess),
"constraints": (), "constraints": (),
"options": {'disp': True, "options": {'disp': True,
'maxiter': 1000, 'maxiter': 1000,
@ -363,14 +366,16 @@ class REEPS:
:param objective: (func) the objective function :param objective: (func) the objective function
:param x_guess: (np.ndarray) the initial guess (always 1) :param x_guess: (np.ndarray) the initial guess (always 1)
:param optimizer_kwargs: (dict) dictionary of options for minimize
:returns: (np.ndarray) Optimized parameters :returns: (np.ndarray) Optimized parameters
""" """
optimizer_kwargs = {"method": 'SLSQP', if optimizer_kwargs is None:
"bounds": [(1e-1, 1e1)] * len(x_guess), optimizer_kwargs = {"method": 'SLSQP',
"constraints": (), "bounds": [(1e-1, 1e1)] * len(x_guess),
"options": {'disp': True, "constraints": (),
'maxiter': 1000, "options": {'disp': True,
'ftol': 1e-6}} 'maxiter': 1000,
'ftol': 1e-6}}
res = minimize(objective, x_guess, **optimizer_kwargs) res = minimize(objective, x_guess, **optimizer_kwargs)
est_parameters = res.x est_parameters = res.x
return est_parameters return est_parameters
@ -450,7 +455,7 @@ class REEPS:
"""Change list of Cantera solutions by inputting """Change list of Cantera solutions by inputting
new xml file name and phase names new xml file name and phase names
Also runs set_in_moles to set initial molality to 1 g/L Also runs set_in_moles to set feed volume to 1 L
:param phases_xml_filename: (str) xml file with parameters :param phases_xml_filename: (str) xml file with parameters
for equilibrium calc for equilibrium calc
@ -656,7 +661,7 @@ class REEPS:
This function also calls update_predicted_dict This function also calls update_predicted_dict
:param feed_vol: (float) feed volume of mixture (g/L) :param feed_vol: (float) feed volume of mixture (L)
""" """
phases_copy = self._phases.copy() phases_copy = self._phases.copy()
exp_df = self._exp_df.copy() exp_df = self._exp_df.copy()
@ -809,7 +814,7 @@ class REEPS:
"with at least 2 arguments: " "with at least 2 arguments: "
"f(objective_func,x_guess, kwargs)") "f(objective_func,x_guess, kwargs)")
if optimizer == 'SLSQP': if optimizer == 'SLSQP':
optimizer = self.slsqp_optimizer optimizer = self.scipy_minimize
self._optimizer = optimizer self._optimizer = optimizer
return None return None
@ -939,7 +944,9 @@ class REEPS:
i = 0 i = 0
for species_name in opt_dict.keys(): for species_name in opt_dict.keys():
for thermo_prop in opt_dict[species_name].keys(): for thermo_prop in opt_dict[species_name].keys():
opt_dict[species_name][thermo_prop] *= x[i] if not np.isnan(
x[i]): # if nan, do not update xml with nan
opt_dict[species_name][thermo_prop] *= x[i]
i += 1 i += 1
self.update_xml(opt_dict, temp_xml_file_path) self.update_xml(opt_dict, temp_xml_file_path)
@ -969,9 +976,12 @@ class REEPS:
with optimizer. Returns dictionary with opt_dict structure with optimizer. Returns dictionary with opt_dict structure
:param objective_function: (function) function to compute objective :param objective_function: (function) function to compute objective
If 'None', last set objective or default function is used
:param optimizer: (function) function to perform optimization :param optimizer: (function) function to perform optimization
:param optimizer_kwargs: (dict) arguments for optimizer If 'None', last set optimizer or default is used
:param objective_kwargs: (dict) arguments for objective function :param optimizer_kwargs: (dict) optional arguments for optimizer
:param objective_kwargs: (dict) optional arguments
for objective function
:returns opt_dict: (dict) optimized opt_dict. Has identical structure :returns opt_dict: (dict) optimized opt_dict. Has identical structure
as opt_dict as opt_dict
""" """
@ -1043,6 +1053,8 @@ class REEPS:
def parity_plot(self, def parity_plot(self,
compared_value=None, compared_value=None,
color_axis=None,
plot_title=None,
save_path=None, save_path=None,
print_r_squared=False): print_r_squared=False):
""" """
@ -1052,6 +1064,16 @@ class REEPS:
:param compared_value: (str) Quantity to compare predicted and :param compared_value: (str) Quantity to compare predicted and
experimental data. Can be any column containing "eq" in exp_df i.e. experimental data. Can be any column containing "eq" in exp_df i.e.
h_eq, z_eq, {RE}_d_eq, etc. h_eq, z_eq, {RE}_d_eq, etc.
:param plot_title: (str or boolean)
If None (default): Plot title will be generated from compared_value
Recommend to just explore. If h_eq, plot_title is
"H^+ eq conc".
If str: Plot title will be plot_title string
If "False": No plot title
:param color_axis: (dict)
:param save_path: (str) save path for parity plot :param save_path: (str) save path for parity plot
:param print_r_squared: (boolean) To plot or not to plot r-squared :param print_r_squared: (boolean) To plot or not to plot r-squared
value. Prints 2 places past decimal value. Prints 2 places past decimal
@ -1071,33 +1093,57 @@ class REEPS:
name_breakdown = re.findall('[^_\W]+', compared_value) name_breakdown = re.findall('[^_\W]+', compared_value)
compared_species = name_breakdown[0] compared_species = name_breakdown[0]
if compared_species == 'h': if compared_species == 'h':
species_name = '$H^+$' default_title = '$H^+$ eq. conc. (mol/L)'
elif compared_species == 'z': elif compared_species == 'z':
species_name = extractant_name default_title = '{0} eq. conc. (mol/L)'.format(extractant_name)
else: else:
phase = name_breakdown[1] phase = name_breakdown[1]
if phase == 'aq': if phase == 'aq':
re_charge = re_charges[re_species_list.index(compared_species)] re_charge = re_charges[re_species_list.index(compared_species)]
species_name = '$%s^{%d+}$' % (compared_species, re_charge) default_title = '$%s^{%d+}$ eq. conc. (mol/L)' \
% (compared_species, re_charge)
elif phase == 'd': elif phase == 'd':
species_name = '{0} distribution ratio'.format( default_title = '{0} distribution ratio'.format(
compared_species) compared_species)
else: else:
species_name = '{0} complex'.format(compared_species) default_title = '{0} complex eq. conc. (mol/L)'.format(
compared_species)
fig, ax = plt.subplots() fig, ax = plt.subplots()
p1 = sns.scatterplot(meas, pred, color="r", if color_axis is None:
label="{0} eq. conc. (mol/L)".format( sns.scatterplot(meas, pred, color="r",
species_name), legend=False)
legend=False) else:
key = list(color_axis.keys())[0]
value = list(color_axis.values())[0]
if key == 'predicted':
y = self.get_predicted_dict()[value]
elif key == 'measured':
y = self.get_exp_df()[value].values
else:
raise Exception('color_axis must be a dictionary with key'
'"predicted" or "measured"')
y = np.array(y)
meas = np.array(meas)
pred = np.array(pred)
p1 = ax.scatter(meas, pred, c=y, alpha=1, cmap='viridis')
c_bar = fig.colorbar(p1, format='%.2f')
# Fix next line. value is just the dictionary value.
c_bar.set_label(value, rotation=270, labelpad=20)
sns.lineplot(min_max_data, min_max_data, color="b", label="") sns.lineplot(min_max_data, min_max_data, color="b", label="")
if print_r_squared: if print_r_squared:
p1.text(min_max_data[0], ax.text(min_max_data[0],
min_max_data[1] * 0.9, min_max_data[1] * 0.9,
'$R^2$={0:.2f}'.format(self.r_squared(compared_value))) '$R^2$={0:.2f}'.format(self.r_squared(compared_value)))
plt.legend(loc='lower right') # plt.legend(loc='lower right')
else: # else:
plt.legend() # plt.legend()
ax.set(xlabel='Measured', ylabel='Predicted') ax.set(xlabel='Measured', ylabel='Predicted')
if plot_title is None:
ax.set_title(default_title)
elif isinstance(plot_title, str):
ax.set_title(plot_title)
plt.show() plt.show()
if save_path is not None: if save_path is not None:
plt.savefig(save_path, bbox_inches='tight') plt.savefig(save_path, bbox_inches='tight')
@ -1110,7 +1156,7 @@ class REEPS:
:param compared_value: (str) Quantity to compare predicted and :param compared_value: (str) Quantity to compare predicted and
experimental data. Can be any column containing "eq" in exp_df i.e. experimental data. Can be any column containing "eq" in exp_df i.e.
h_eq, z_eq, {RE}_d_eq, etc. h_eq, z_eq, {RE}_d_eq, etc. default is {RE}_aq_eq
""" """
exp_df = self.get_exp_df() exp_df = self.get_exp_df()
predicted_dict = self.get_predicted_dict() predicted_dict = self.get_predicted_dict()

6
tests/test_multi_reeps.py
Unescape View File

@ -15,8 +15,10 @@ def optimizer(func, x_guess):
ub = np.array([1e1]) ub = np.array([1e1])
bounds = (lb, ub) bounds = (lb, ub)
options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9} options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9}
mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100, dimensions=1, mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100,
options=options, bounds=bounds) dimensions=1,
options=options,
bounds=bounds)
f_opt, x_opt = mini_optimizer.optimize(func, iters=100) f_opt, x_opt = mini_optimizer.optimize(func, iters=100)
return x_opt return x_opt

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