def plot_param_learning_curves():
x_data, _y, full_data = data_get.get_data('as7262 mango', average=False)
pls = PLS(n_components=6)
print(full_data.columns)
currents = full_data['LED current'].unique()
times = full_data['integration time'].unique()
print(currents, times)
print(full_data['saturation check'].unique())
figure, axes, = plt.subplots(len(currents),
len(times),
figsize=(9, 12),
constrained_layout=True)
figure.suptitle("Parameter scan of new AS7262 Mango data")
# figure.suptitle("Gradient Boosting Regressor fit\nAS7262 Betel data")
# axes_ = [axes[0][0], axes[0][1], axes[0][2], axes[0][3],
# axes[1][0], axes[1][1], axes[1][2], axes[1][3],
# axes[2][0], axes[2][1], axes[2][2], axes[2][3],
# axes[3][0], axes[3][1], axes[3][2], axes[3][3],
# axes[4][0], axes[4][1], axes[4][2], axes[4][3],]
current_i = 0
time_i = 0
for current in currents:
for time in times:
X, Y = data_get.get_data("as7262 mango",
integration_time=time,
led_current=current,
return_type="XY")
X = StandardScaler().fit_transform(X)
X = PolynomialFeatures().fit_transform(X)
Y = Y['Total Chlorophyll (µg/mg)']
title = str(time * 2.8) + " ms " + current
print(title)
plot_learning_curve(pls,
title,
X,
Y,
cv=cv,
ax=axes[current_i][time_i],
ylim=[-0.3, -.1])
time_i += 1
time_i = 0
current_i += 1
def call(self):
#create d and g models
self.d_model = Discriminator(y_input=self.y_input,
x_input=self.x_input,
n_filters=self.gfilters /
(2**self.n_convols),
n_convols=self.n_convols,
rgb=self.rgb).build()
self.g_model = Generator(self.y_input,
self.x_input,
n_filters=self.gfilters,
n_convols=self.n_convols,
rgb=self.rgb).build()
#create gan using d and g models
self.infogan = GAN(self.d_model,
self.g_model,
self.results_directory,
noise_size=62,
relu_alpha=self.relu_alpha)
#compile gan
self.infogan.compile(
d_optimizer=keras.optimizers.Adam(learning_rate=self.d_learn),
g_optimizer=keras.optimizers.Adam(learning_rate=self.g_learn))
#get data ready
self.X = get_data(self.image_directory, self.y_input, self.x_input,
self.batch_size, self.rgb, self.n_convols)
"""
__author__ = "Kyle Vitatus Lopin"
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_validate, RepeatedKFold
from sklearn.pipeline import make_pipeline
# local files
import data_get
import full_regrs
import processing
filename = "as7262 new mango results.xlsx"
x_data, _y, data = data_get.get_data('as7262 mango')
all_regressors = full_regrs.get_all_regrs()
all_transformers = full_regrs.get_transformers()
print(all_regressors)
print(x_data)
chloro_types = [
'Chlorophyll a (µg/mg)', 'Chlorophyll b (µg/mg)',
'Total Chlorophyll (µg/mg)'
]
_y = _y['Total Chlorophyll (µg/mg)']
print(_y)
cv = RepeatedKFold(n_splits=4, n_repeats=15)
# Copyright (c) 2019 Kyle Lopin (Naresuan University) <*****@*****.**>
"""
"""
__author__ = "Kyle Vitatus Lopin"
import pandas as pd
from sklearn.linear_model import LassoCV
from sklearn.model_selection import cross_validate, ShuffleSplit
from sklearn.preprocessing import PolynomialFeatures
# local files
import data_get
est = LassoCV()
cv = ShuffleSplit(n_splits=100)
# single read
for read in [1, 2, 3]:
X, Y = data_get.get_data("as7262 mango", integration_time=200, current="25 mA",
position=read, return_type="XY")
from sklearn.linear_model import LassoCV, SGDRegressor
from sklearn.model_selection import LeaveOneOut, LeaveOneGroupOut
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, FunctionTransformer, PolynomialFeatures
# local files
import data_get
import processing
import sfs
plt.style.use('seaborn')
# fitting_data = pd.read_csv('as7262_roseapple.csv')
x_data, _, fitting_data = data_get.get_data('as7262 mango',
integration_time=150,
led_current="12.5 mA",
average=False)
# x_data, _, fitting_data = data_get.get_data('as7262 mango', integration_time=200,
# led_current="12.5 mA",
# read_number=2)
# x_data1, _, fitting_data2 = data_get.get_data('as7262 mango', integration_time=50,
# led_current="12.5 mA",
# read_number=2)
# x_data2, _, fitting_data3 = data_get.get_data('as7262 mango', integration_time=150,
# led_current="25 mA",
# read_number=2)
#
# x_data = pd.concat([x_data, x_data1, x_data2], axis=1)
# print(fitting_data.columns)
# x_data["Leaf number"] = fitting_data['Leaf number']
# installed libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# local files
import data_get
import processing
plt.style.use('seaborn')
# data = pd.read_csv("as7262_mango.csv")
# x_data, data = data_getter.get_data('as7262 roseapple')
x_data, _, data = data_get.get_data(
'as7262 mango',
integration_time=200,
led_current="50 mA",
)
print(data)
print(data.columns)
# data = data.loc[(data['position'] == 'pos 2')]
# data = data.loc[(data['integration time'] == 3)]
# data = data.groupby('Leaf number', as_index=True).mean()
accent_column = data['Total Chlorophyll (µg/mg)'].to_numpy()
# accent_column = np.ones_like(data.iloc[:, 1])
accent_column = accent_column / max(accent_column)
# print(accent_column/max(accent_column))
# alphas = np.linspace(0.1, 1, 10)
colors = np.zeros((data.shape[0], 4))
from sklearn.linear_model import LassoCV, SGDRegressor, LinearRegression
from sklearn.model_selection import RepeatedKFold
from sklearn.neural_network import MLPRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, RobustScaler, FunctionTransformer, PolynomialFeatures
# local files
import data_get
plt.style.use('seaborn')
# fitting_data = pd.read_csv('as7262_roseapple.csv')
x_data, _, fitting_data = data_get.get_data('as7262 mango', integration_time=150,
led_current="12.5 mA",
read_number=2)
# regr = PLSRegression(n_components=9)
# regr = LassoCV(max_iter=5000)
regr = LinearRegression()
y = fitting_data['Total Chlorophyll (µg/cm2)']
x_scaled_np = StandardScaler().fit_transform(x_data)
x_scaled_np = PolynomialFeatures(degree=2).fit_transform(x_scaled_np)
print(y)
print(x_scaled_np)
# installed libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.cross_decomposition import PLSRegression
from sklearn import linear_model
from sklearn.model_selection import cross_validate, GroupShuffleSplit, RepeatedKFold
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
# local files
import data_get
plt.style.use('seaborn')
x_data, _y, full_data = data_get.get_data('as7262 mango', average=False)
print(full_data.columns)
currents = full_data['LED current'].unique()
times = full_data['integration time'].unique()
print(currents, times)
print(full_data['saturation check'].unique())
pls = PLSRegression(n_components=6)
# pls = linear_model.LinearRegression()
cv = RepeatedKFold(n_splits=5, n_repeats=20)
cv_group = GroupShuffleSplit(n_splits=200)
scores = []
labels = []
errors = []
training_scores = []
training_errors = []
# installed libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.optimize import curve_fit
from sklearn.metrics import mean_absolute_error, r2_score
from sklearn.preprocessing import RobustScaler, StandardScaler
# local files
import data_get
import processing
plt.style.use('seaborn')
# fitting_data = pd.read_csv('as7262_roseapple.csv')
# fitting_data = pd.read_csv("as7262_ylang.csv")
x_data, _y, fitting_data = data_get.get_data('as7262 mango')
print(fitting_data.columns)
# fitting_data = fitting_data.loc[(fitting_data['Total Chlorophyll (ug/ml)'] < 0.5)]
# fitting_data = fitting_data.loc[(fitting_data['LED current'] == 2)]
# fitting_data = fitting_data.loc[(fitting_data['position'] == 'pos 2')]
# print(fitting_data)
fitting_data = fitting_data.groupby('Leaf number', as_index=True).mean()
#
# fitting_data = fitting_data.drop(["Leaf: 50"])
data_columns = []
for column in fitting_data.columns:
if 'nm' in column:
data_columns.append(column)
#
spectrum_data = fitting_data[data_columns]