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Fixed some compatibility issues
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@robinthibaut
robinthibaut committed Feb 16, 2023
1 parent 7c752e6 commit 50dd4e8
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Showing 9 changed files with 29 additions and 396 deletions.
26 changes: 13 additions & 13 deletions docs/examples/demo.ipynb
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186 changes: 2 additions & 184 deletions examples/demo_visualization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
import numpy as np
import pandas as pd
import seaborn as sns
from loguru import logger
from matplotlib import pyplot as plt
from matplotlib.patches import Polygon
from sklearn.pipeline import Pipeline
Expand All @@ -17,7 +16,7 @@

from examples.demo_config import Setup
from skbel import utils
from skbel.goggles import explained_variance, _proxy_annotate, _proxy_legend, pca_scores
from skbel.goggles import _proxy_annotate, _proxy_legend
from skbel.spatial import (
contours_vertices,
grid_parameters,
Expand Down Expand Up @@ -189,7 +188,7 @@ def whpa_plot(
if whpa.ndim > 2: # New approach is to plot filled contours
new_grf = 1 # Refine grid
_, _, new_x, new_y = refine_machine(xlim, ylim, new_grf=new_grf)
xys, nrow, ncol = grid_parameters(x_lim=xlim, y_lim=ylim, grf=new_grf)
xys, nrow, ncol, _ = grid_parameters(x_lim=xlim, y_lim=ylim, grf=new_grf)
vertices = contours_vertices(x=x, y=y, arrays=whpa)
b_low = binary_stack(xys=xys, nrow=nrow, ncol=ncol, vertices=vertices)
contour = plt.contourf(
Expand Down Expand Up @@ -1045,53 +1044,6 @@ def plot_wells(wells: Setup.Wells, well_ids: list = None, markersize: float = 4.
s += 1


def plot_pc_ba(
bel,
base_dir: str = None,
root: str = None,
w: str = None,
data: bool = False,
target: bool = False,
):
"""Comparison between original variables and the same variables back-
transformed with n PCA components.
:param w:
:param base_dir:
:param bel:
:param root:
:param data:
:param target:
:return:
"""

if isinstance(root, (list, tuple)):
if len(root) > 1:
logger.error("Input error")
return
else:
root = root[0]

subdir = os.path.join(base_dir, root)

if data:
# Plot parameters for predictor
xlabel = "Observation index number"
ylabel = "Concentration ($g/m^{3})$"
factor = 1000
labelsize = 11
d_pca_inverse_plot(
bel,
root=root,
xlabel=xlabel,
ylabel=ylabel,
labelsize=labelsize,
factor=factor,
fig_dir=os.path.join(subdir, w, "pca"),
)
if target:
h_pca_inverse_plot(bel, fig_dir=os.path.join(subdir, w, "pca"))


def plot_whpa(bel, base_dir):
"""
Expand Down Expand Up @@ -1125,137 +1077,3 @@ def plot_whpa(bel, base_dir):
labels=labels,
fig_file=os.path.join(base_dir, "whpa_training.png"),
)


def pca_vision(
bel,
base_dir: str,
root: str or list = None,
w: str = None,
d: bool = True,
h: bool = True,
scores: bool = True,
exvar: bool = True,
before_after: bool = True,
labels: bool = True,
):
"""Loads PCA pickles and plot scores for all folders.
:param before_after:
:param base_dir:
:param bel: BEL model
:param w:
:param labels:
:param root: str:
:param d: bool:
:param h: bool:
:param scores: bool:
:param exvar: bool:
:return:
"""

if root is None:
root = ""
if w is None:
w = ""

subdir = jp(base_dir, root, w, "pca")

if d:
fig_file = os.path.join(subdir, "d_scores.png")
if scores:
pca_scores(training=bel.X_pc, prediction=bel.X_obs_pc, n_comp=bel.X_n_pc, fig_file=fig_file, labels=labels)
# Explained variance plots
if exvar:
fig_file = os.path.join(subdir, "d_exvar.png")
explained_variance(n_components, evr, n_cut=bel.X_n_pc, fig_file=fig_file)
if before_after:
plot_pc_ba(bel, base_dir=base_dir, root=root, w=w, data=True, target=False)
if h:
# Transform and split
h_pc_training = bel.Y_pc
try:
Y_obs = check_array(bel.Y_obs, allow_nd=True)
except ValueError:
Y_obs = check_array(bel.Y_obs.to_numpy().reshape(1, -1))
h_pc_prediction = bel.Y_pre_processing.transform(Y_obs)
# Plot
fig_file = os.path.join(subdir, "h_pca_scores.png")
if scores:
pca_scores(training=h_pc_training, prediction=h_pc_prediction, n_comp=bel.Y_n_pc, fig_file=fig_file,
labels=labels)
# Explained variance plots
if exvar:
fig_file = os.path.join(subdir, "h_pca_exvar.png")
explained_variance(n_components, evr, n_cut=bel.Y_n_pc, fig_file=fig_file)
if before_after:
plot_pc_ba(bel, base_dir=base_dir, root=root, w=w, data=False, target=True)


def d_pca_inverse_plot(
bel,
root,
factor: float = 1.0,
xlabel: str = None,
ylabel: str = None,
labelsize: float = 11.0,
fig_dir: str = None,
show: bool = False,
):
"""Plot used to compare the reproduction of the original physical space
after PCA transformation.
:param bel: BEL model
:param xlabel:
:param ylabel:
:param labelsize:
:param factor:
:param fig_dir: str:
:param show: bool:
:return:
"""

shape = bel.X_shape
v_pc = bel.X_obs_pc

nc = bel.X_pre_processing["pca"].n_components_
dummy = np.zeros((1, nc))
dummy[:, : v_pc.shape[1]] = v_pc

v_pred = bel.X_pre_processing.inverse_transform(dummy).reshape((-1,) + shape)
to_plot = np.copy(bel.X_obs).reshape((-1,) + shape)

cols = ["r" for _ in range(shape[1])]
highlights = [i for i in range(shape[1])]
curves(cols=cols, tc=to_plot, factor=factor, highlight=highlights, conc=True)

cols = ["b" for _ in range(shape[1])]
curves(cols=cols, tc=v_pred, factor=factor, highlight=highlights, conc=True)

# Add title inside the box
an = ["A"]
legend_a = _proxy_annotate(annotation=an, loc=2, fz=14)
_proxy_legend(
legend1=legend_a,
colors=["red", "blue"],
labels=["Physical", "Back transformed"],
marker=["-", "-"],
loc=1,
)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.tick_params(labelsize=labelsize)

# Increase y axis by a small percentage for annotation in upper left corner
yrange = np.max(to_plot * factor) * 1.15
plt.ylim([0, yrange])

if fig_dir is not None:
utils.dirmaker(fig_dir)
plt.savefig(jp(fig_dir, f"{root}_d.png"), dpi=300, transparent=False)
if show:
plt.show()
plt.close()
elif show:
plt.show()
plt.close()
62 changes: 0 additions & 62 deletions examples/whpa_bnn.py
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This file was deleted.

14 changes: 7 additions & 7 deletions examples/whpa_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,14 +2,14 @@

import os
from os.path import join as jp
import joblib

import pandas as pd
from sklearn.cross_decomposition import CCA
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, PowerTransformer

import demo_visualization as myvis
from demo_visualization import plot_results

from skbel import utils
from skbel import BEL
Expand Down Expand Up @@ -84,10 +84,10 @@ def init_bel():
]

# %% Load dataset
X_train = pd.read_pickle(jp(data_dir, "X_train.pkl"))
X_test = pd.read_pickle(jp(data_dir, "X_test.pkl"))
y_train = pd.read_pickle(jp(data_dir, "y_train.pkl"))
y_test = pd.read_pickle(jp(data_dir, "y_test.pkl"))
X_train = joblib.load(jp(data_dir, "X_train.pkl")).to_numpy()
X_test = joblib.load(jp(data_dir, "X_test.pkl")).to_numpy().reshape(1, -1)
y_train = joblib.load(jp(data_dir, "y_train.pkl")).to_numpy()
y_test = joblib.load(jp(data_dir, "y_test.pkl")).to_numpy().reshape(1, -1)

# %% Initiate BEL model
model = init_bel()
Expand All @@ -114,7 +114,7 @@ def init_bel():
# %% Visualization

# Plot raw data
myvis.plot_results(
plot_results(
model,
y_predicted=y_predicted,
X=X_train,
Expand Down
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