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ColumnWindow.py
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ColumnWindow.py
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import os
from PyQt5.QtWidgets import *
from PyQt5 import QtWidgets
from PyQt5.uic import loadUi
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as Canvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from matplotlib.figure import Figure
import numpy as np
from sklearn import svm
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import test
class ColWin(QMainWindow):
def __init__(self, parent, signal, filenames, t_pca, p_pca, t_der1, p_der1, t_der2, p_der2, tr_pca, pr_pca, waves,):
super(ColWin, self).__init__(parent)
print(os.path.abspath(os.curdir))
loadUi("interface\\ColumnWindow.ui", self)
self.parent = parent
self.t_matrix = None
self.p_matrix = None
self.waves_for_graph = None
self.Columns = []
self.signal = signal
self.filenames = filenames
self.t_matrix_pca = t_pca
self.p_matrix_pca = p_pca
self.t_matrix_der1 = t_der1
self.p_matrix_der1 = p_der1
self.t_matrix_der2 = t_der2
self.p_matrix_der2 = p_der2
self.tr_pca = tr_pca
self.pr_pca = pr_pca
self.waves = waves
self.Columns_int.setPlaceholderText("1,2,3")
self.pushButton.clicked.connect(self.showGraphColumn)
self.colGraph = MplWidget(self.signal, self.colGraph)
self.CloseButton.clicked.connect(self.closeEvent)
QAction("Quit", self).triggered.connect(self.closeEvent)
if signal == 2:
self.label_3.setText('Введите номер столбцa:')
self.Columns_int.returnPressed.connect(self.pushButton.click)
filenames = []
for i in self.filenames:
filenames.append(i[0])
print("spectra PC2")
for i in [svm.SVC(), KNeighborsClassifier(n_neighbors=4), LogisticRegression(), DecisionTreeClassifier(),
GaussianNB(), LinearDiscriminantAnalysis()]:
mean_list, std_list = test.classifier(input_matrix=self.t_matrix_pca[:, 1], labels=filenames, tr_s=0.8, classif=i, n_iter=100)
print('accuracy:', mean_list[0], std_list[0], '---', 'precision D:', mean_list[1], std_list[1], '---',
'recall D:', mean_list[2], std_list[2])
print('accuracy:', mean_list[0], std_list[0], '---', 'precision M:', mean_list[3], std_list[3], '---',
'recall M:', mean_list[4], std_list[4])
print("_________________________________________________________")
print("spectra deriv PC2")
for i in [svm.SVC(), KNeighborsClassifier(n_neighbors=4), LogisticRegression(), DecisionTreeClassifier(),
GaussianNB(), LinearDiscriminantAnalysis()]:
mean_list, std_list = test.classifier(input_matrix=self.t_matrix_der2[:, 1], labels=filenames, tr_s=0.8, classif=i, n_iter=100)
print('accuracy:', mean_list[0], std_list[0], '---', 'precision D:', mean_list[1], std_list[1], '---',
'recall D:', mean_list[2], std_list[2])
print('accuracy:', mean_list[0], std_list[0], '---', 'precision M:', mean_list[3], std_list[3], '---',
'recall M:', mean_list[4], std_list[4])
print("_________________________________________________________")
print("feachures PC1")
for i in [svm.SVC(), KNeighborsClassifier(n_neighbors=4), LogisticRegression(), DecisionTreeClassifier(),
GaussianNB(), LinearDiscriminantAnalysis()]:
mean_list, std_list = test.classifier(input_matrix=self.tr_pca[:, 0], labels=filenames, tr_s=0.8, classif=i, n_iter=100)
print('accuracy:', mean_list[0], std_list[0], '---', 'precision D:', mean_list[1], std_list[1], '---',
'recall D:', mean_list[2], std_list[2])
print('accuracy:', mean_list[0], std_list[0], '---', 'precision M:', mean_list[3], std_list[3], '---',
'recall M:', mean_list[4], std_list[4])
print("_________________________________________________________")
print("feachures PC2")
for i in [svm.SVC(), KNeighborsClassifier(n_neighbors=4), LogisticRegression(), DecisionTreeClassifier(),
GaussianNB(), LinearDiscriminantAnalysis()]:
mean_list, std_list = test.classifier(input_matrix=self.tr_pca[:, 1], labels=filenames, tr_s=0.8, classif=i, n_iter=100)
print('accuracy:', mean_list[0], std_list[0], '---', 'precision D:', mean_list[1], std_list[1], '---',
'recall D:', mean_list[2], std_list[2])
print('accuracy:', mean_list[0], std_list[0], '---', 'precision M:', mean_list[3], std_list[3], '---',
'recall M:', mean_list[4], std_list[4])
print("_________________________________________________________")
print("feachures PC1-PC2")
for i in [svm.SVC(), KNeighborsClassifier(n_neighbors=4), LogisticRegression(max_iter=1000), DecisionTreeClassifier(),
GaussianNB(), LinearDiscriminantAnalysis()]:
mean_list, std_list = test.classifier(input_matrix=self.tr_pca[:, [0, 1]], labels=filenames, tr_s=0.8, classif=i, n_iter=100)
print('accuracy:', mean_list[0], std_list[0], '---', 'precision D:', mean_list[1], std_list[1], '---', 'recall D:', mean_list[2], std_list[2])
print('accuracy:', mean_list[0], std_list[0], '---', 'precision M:', mean_list[3], std_list[3], '---', 'recall M:', mean_list[4], std_list[4])
print("_________________________________________________________")
def closeEvent(self, event):
self.parent.show()
self.close()
def showGraphColumn(self):
self.Columns = []
Columns_temp = self.Columns_int.text().replace(' ', '').replace('.', ',').split(',')
for j in Columns_temp:
self.Columns.append(int(j))
self.radioButtonChecking()
try:
if self.signal == 1:
self.plotScores()
elif self.signal == 2:
self.plotLoadings()
elif self.signal == 3:
self.plot3D()
except Exception as error:
print(error)
pass
def radioButtonChecking(self):
if self.SpectraButton.isChecked():
self.t_matrix = self.t_matrix_pca
self.p_matrix = self.p_matrix_pca
self.waves_for_graph = self.waves[0]
elif self.Derivative_1_Button.isChecked():
self.t_matrix = self.t_matrix_der1
self.p_matrix = self.p_matrix_der1
self.waves_for_graph = self.waves[1]
elif self.Derivative_2_Button.isChecked():
self.t_matrix = self.t_matrix_der2
self.p_matrix = self.p_matrix_der2
self.waves_for_graph = self.waves[2]
elif self.RatioWave_button.isChecked():
self.t_matrix = self.tr_pca
self.p_matrix = self.pr_pca
self.waves_for_graph = self.p_matrix[:, self.Columns[1]-1]
def prepare_data(self, *args):
samples = {
"D": [],
"M": [],
"N": [],
"O": [],
}
for item in range(len(args)):
for key in samples:
samples[key].append([])
for index in range(len(self.filenames)):
for axis in range(len(args)):
if (self.filenames[index][0] == 'P') or (self.filenames[index][0] == 'M'):
samples['M'][axis].append(args[axis][index])
elif self.filenames[index][0] == 'N':
samples['N'][axis].append(args[axis][index])
elif self.filenames[index][0] == 'D':
samples['D'][axis].append(args[axis][index])
elif (self.filenames[index][0] == 'O') or (self.filenames[index][0] == 'B'):
samples['O'][axis].append(args[axis][index])
return samples
def plotScores(self):
self.colGraph.canvas.ax.clear()
x = self.t_matrix[:, self.Columns[0] - 1]
y = self.t_matrix[:, self.Columns[1] - 1]
samples = self.prepare_data(x, y, self.filenames)
colors = {'M': 'red', 'N': 'blue', 'D': 'green', 'O': 'black'}
markers = {'M': 'o', 'N': '^', 'D': '*', 'O': 's'}
for key, value in samples.items():
for index in range(len(self.filenames)):
self.colGraph.canvas.ax.scatter(value[0], value[1],
color=colors[key], marker=markers[key], alpha=1, zorder=10, s=80)
# for item in range(len(value[0])):
# self.colGraph.canvas.ax.annotate(value[-1][item], (value[0][item], value[1][item]))
self.colGraph.canvas.ax.tick_params(axis='both', which='major', labelsize=15)
self.colGraph.canvas.ax.tick_params(axis='both', which='minor', labelsize=15)
self.colGraph.canvas.draw()
def plotLoadings(self):
xs = self.waves_for_graph
ys = list(self.p_matrix[:, self.Columns[0] - 1])
self.colGraph.canvas.ax.clear()
self.colGraph.canvas.ax.scatter(xs, ys, color="black", marker="o", s=12)
wave = [1690, 1684, 1682.5, 1672, 1664, 1652.5, 1647, 1631, 1629.5]
struct = ['Beta-turns', 'Beta-turns', 'Beta-sheets', 'Beta-turns', 'Beta-turns', 'Alpha-helices',
'Random-coil', 'Beta-sheets', 'Beta-sheets', ]
color = ['green', 'green', 'blue', 'green', 'green', 'red', 'orange', 'blue', 'blue']
width = [1, 1, 12.5, 1, 1, 3, 5, 2, 1, 11.5]
if self.RatioWave_button.isChecked():
labels = ['M$_{I}$/N$_{1}$', 'M$_{I}$/M$_{S}$', 'M$_{I}$/N$_{2}$', 'M$_{I}$/M$_{T}$', 'M$_{I}$/N$_{3}$', "M$_{I}$/M$_{II'}$",
'M$_{S}$/N$_{1}$', 'N$_{1}$/N$_{2}$', 'N$_{1}$/M$_{T}$', 'N$_{1}$/N$_{3}$', "N$_{1}$/M$_{II'}$",
'M$_{S}$/N$_{2}$', 'M$_{S}$/M$_{T}$', 'M$_{S}$/N$_{3}$', "M$_{S}$/M$_{II'}$",
'N$_{2}/M$_{T}$', 'N$_{2}$/N$_{3}$', "N$_{2}$/M$_{II'}$",
'M$_{T}$/N$_{3}$', "M$_{T}$/M$_{II'}$",
"N$_{3}$/M$_{II'}$",
'M$_{I}$', 'N$_{1}$', 'M$_{S}$', 'N$_{2}$', 'M$_{T}$', 'N$_{3}$', "M$_{II'}$"]
for i, txt in enumerate(labels):
self.colGraph.canvas.ax.annotate(txt, (xs[i], ys[i]))
if max(xs) >= 1652.5 and min(xs) <= 1629.5:
for ind in range(len(wave)):
self.colGraph.canvas.ax.axvline(x=wave[ind], color=color[ind], label=struct[ind],
linewidth=width[ind], alpha=0.5)
self.colGraph.canvas.ax.invert_xaxis()
self.colGraph.canvas.draw()
def plot3D(self):
self.colGraph.canvas.ax.clear()
x = self.t_matrix[:, self.Columns[0] - 1]
y = self.t_matrix[:, self.Columns[1] - 1]
z = self.t_matrix[:, self.Columns[2] - 1]
samples = self.prepare_data(x, y, z, self.filenames)
colors = {'M': 'red', 'N': 'blue', 'D': 'green', 'O': 'black'}
markers = {'M': 'o', 'N': '^', 'D': '*', 'O': 's'}
for key, value in samples.items():
self.colGraph.canvas.ax.scatter(samples[key][0], samples[key][1], samples[key][2],
color=colors[key], marker=markers[key], alpha=1)
for item in range(len(value[0])):
self.colGraph.canvas.ax.annotate(value[-1][item], (value[0][item], value[1][item]))
self.colGraph.canvas.draw()
class MplCanvas(Canvas):
def __init__(self, signal):
dpi = 100
self.fig = Figure(figsize=(1240/dpi, 600/dpi), dpi=dpi)
if signal == 1 or signal == 2:
self.ax = self.fig.add_subplot(111)
elif signal == 3:
self.ax = self.fig.add_subplot(111, projection='3d')
for label in (self.ax.get_xticklabels() + self.ax.get_yticklabels()):
label.set_fontsize(16)
super(MplCanvas, self).__init__(self.fig)
Canvas.setSizePolicy(self, QSizePolicy.Expanding, QSizePolicy.Expanding)
Canvas.updateGeometry(self)
class MplWidget(QtWidgets.QWidget):
def __init__(self, signal, parent=None):
super().__init__()
QtWidgets.QWidget.__init__(self, parent)
self.canvas = MplCanvas(signal)
self.vbl = QtWidgets.QVBoxLayout()
self.vbl.addWidget(self.canvas)
self.toolbar = NavigationToolbar(self.canvas, self)
self.vbl.addWidget(self.toolbar)
self.setLayout(self.vbl)