def pls_learning(num_components, num_vars):
print(1)
data_full = pd.read_csv('mango_flouro_rows.csv')
data_full = data_full[data_full['position'] == 'pos 2']
y_name = 'Total Chlorophyll (ug/ml)'
# y_name = 'Chlorophyll b (ug/ml)'
y_data = data_full[y_name]
x_data = data_full[x_data_columns]
x_data = get_best_pls_variables(x_data, y_data, num_components, num_vars)
cv = GroupShuffleSplit(n_splits=100, test_size=0.3, random_state=0)
group_splitter = data_full['Leaf number']
estimator = PLS(num_components)
title = "Learning curve {} components".format(num_components)
plot_learning_curve(estimator,
title,
x_data,
y_data,
cv=cv,
group=group_splitter)
plt.show()
def get_all_regrs():
regrs = {
"Linear regression":
linear_model.LinearRegression(),
# "Perceptron": linear_model.Perceptron(),
"Lars":
linear_model.Lars(),
"Lasso":
linear_model.LassoCV(max_iter=5000),
# "Passive Aggressive": linear_model.PassiveAggressiveRegressor(),
"PLS":
PLS(n_components=3),
"Random Forest":
ensemble.RandomForestRegressor(),
"Gradient Boost":
ensemble.GradientBoostingRegressor(),
"Extra Trees":
ensemble.ExtraTreesRegressor(max_depth=2),
"Ada Boost":
ensemble.AdaBoostRegressor(
base_estimator=tree.DecisionTreeRegressor(max_depth=2),
n_estimators=250),
"Gaussian Process":
gaussian_process.GaussianProcessRegressor(),
# "Isotonic": isotonic.IsotonicRegression(),
"Kernel Ridge":
kernel_ridge.KernelRidge(),
"Ridge CV":
linear_model.RidgeCV(),
# "Exp tranform": TransformedTargetRegressor(regressor=PLS(n_components=3),
# func=np.exp,
# inverse_func=np.log),
# "Log tranform": TransformedTargetRegressor(regressor=PLS(n_components=3),
# func=np.log,
# inverse_func=np.exp),
# "Inv tranform": TransformedTargetRegressor(regressor=PLS(n_components=3),
# func=invert,
# inverse_func=invert),
# "Log regressor": linear_model.LogisticRegressionCV(),
"ML Perceptron":
neural_network.MLPRegressor(max_iter=50000, hidden_layer_sizes=(5, 5)),
"Linear SVR":
linear_svc,
"RBF SVR":
svm.SVR(kernel='rbf'),
"Poly SVR":
svm.SVR(kernel='poly'),
# "Sigmoid SVR": svm.SVR(kernel='sigmoid'),
"Bayesian Ridge":
linear_model.BayesianRidge(),
"Huber":
linear_model.HuberRegressor(),
# "Poisson": linear_model.PoissonRegressor(),
"K-neighbors":
neighbors.KNeighborsRegressor()
}
# "Radius Neighbors": neighbors.RadiusNeighborsRegressor()}
return regrs
def get_best_pls_variables(x, y, num_pls_components, num_varaibles):
x_scaled_np = StandardScaler().fit_transform(x)
x_scaled = pd.DataFrame(x_scaled_np, columns=x.columns)
pls = PLS(num_pls_components)
pls.fit(x_scaled, y)
sorted_coeff = np.argsort(np.abs(pls.coef_[:, 0]))
sorted_coeff = np.flip(sorted_coeff)
columns_to_keep = x.columns[sorted_coeff[:num_varaibles]]
print(columns_to_keep)
return x_scaled[columns_to_keep]
def transform(X, factors, get_model, method, y=None):
if method == "raw" or method is None:
return X
if not factors or factors == "full":
factors = np.prod(X.shape[1:])
if method == "lda":
factors -= 1
if not isinstance(method, str):
raise RuntimeError("Please supply a method name (pca, lda, ica, cca, pls)")
method = method.lower()
if method == "pca":
from sklearn.decomposition import PCA
model = PCA(n_components=factors, whiten=True)
elif method == "lda":
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
model = LDA(n_components=factors)
elif method == "ica":
from sklearn.decomposition import FastICA as ICA
model = ICA(n_components=factors)
elif method == "cca":
from sklearn.cross_decomposition import CCA
model = CCA(n_components=factors)
elif method == "pls":
from sklearn.cross_decomposition import PLSRegression as PLS
model = PLS(n_components=factors)
if str(y.dtype)[:3] not in ("flo", "int"):
y = dummycode(y, get_translator=False)
else:
raise ValueError("Method {} unrecognized!".format(method))
X = rtm(X)
if method in ("lda", "cca", "pls"):
if y is None:
raise RuntimeError("y must be supplied for {}!".format(method))
latent = model.fit_transform(X, y)
else:
if y is not None:
warnings.warn("y supplied for {}. Ignoring!".format(method))
latent = model.fit_transform(X)
if isinstance(latent, tuple):
latent = latent[0]
if get_model:
return latent, model
else:
return latent
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 fit(self, data, resp):
dof = data.shape[1]
n = data.shape[0]
ortho_comp = dof - self.comp
W = np.ndarray(shape=(dof, ortho_comp))
P = np.ndarray(shape=(dof, ortho_comp))
T = np.ndarray(shape=(n, ortho_comp))
# Start with Vector
w = np.transpose(np.matmul(np.transpose(resp), data) / lin.norm(resp))
for i in range(ortho_comp):
t = np.matmul(data, w) / np.matmul(np.transpose(w),
w) # get pls scores
p = np.transpose(
np.matmul(np.transpose(t), data) /
np.matmul(np.transpose(t), t)) # pls loadings
## Get Orthogonal Components
w_ortho = p - ((np.matmul(np.transpose(w), p) /
np.matmul(np.transpose(w), w)) * w)
w_ortho = w_ortho / lin.norm(w_ortho)
t_ortho = np.matmul(data, w_ortho) / np.matmul(
np.transpose(w_ortho), w_ortho)
p_ortho = np.transpose(
np.matmul(np.transpose(t_ortho), data) /
np.matmul(np.transpose(t_ortho), t_ortho))
data = data - np.matmul(t_ortho, np.transpose(p_ortho))
W[:, i] = np.reshape(w_ortho, (dof, ))
P[:, i] = np.reshape(p_ortho, (dof, ))
T[:, i] = np.reshape(t_ortho, (n, ))
self.data_p = data
self.data_o = np.matmul(T, np.transpose(P))
self.W_o = W
self.T_o = T
self.P_o = P
## Build PLS Regression from data.P
pls = PLS(n_components=self.comp).fit(self.data_p, resp)
self.analysis = pls
self.rotated_data = pls.transform(self.data_p)
return (self)
def _pls_regression_train(table, feature_cols, label_cols, n_components=2, scale=True, max_iter=500, tol=1e-6):
pls_model = PLS(n_components=n_components, scale=scale, max_iter=max_iter, tol=tol)
_, features = check_col_type(table, feature_cols)
_, labels = check_col_type(table, label_cols)
pls_model.fit(features, labels)
predict = pls_model.predict(features)
_mean_absolute_error = mean_absolute_error(labels, predict)
_mean_squared_error = mean_squared_error(labels, predict)
_r2_score = r2_score(labels, predict)
result_table = pd.DataFrame.from_items([
['Metric', ['Mean Absolute Error', 'Mean Squared Error', 'R2 Score']],
['Score', [_mean_absolute_error, _mean_squared_error, _r2_score]]
])
label_name = {
'n_components': 'Number of components',
'scale': "Scale",
'max_iter': 'Max iteration',
'tol': 'Tolerance'
}
get_param = pls_model.get_params()
param_table = pd.DataFrame.from_items([
['Parameter', list(label_name.values())],
['Value', [get_param[x] for x in list(label_name.keys())]]
])
rb = BrtcReprBuilder()
rb.addMD(strip_margin("""
| ### PLS Regression Result
| {result}
| ### Parameters
| {list_parameters}
""".format(result=pandasDF2MD(result_table), list_parameters=pandasDF2MD(param_table)
)))
model = _model_dict('pls_regression_model')
model['feature_cols'] = feature_cols
model['label'] = label_cols
model['mean_absolute_error'] = _mean_absolute_error
model['mean_squared_error'] = _mean_squared_error
model['r2_score'] = _r2_score
model['max_iter'] = max_iter
model['tol'] = tol
model['pls_model'] = pls_model
model['_repr_brtc_'] = rb.get()
return {'model': model}
res_data = res_data[y_name]
x_data_column = data["550 nm"]
def model(x, a, b, c):
return a * np.exp(-b * x) + c
fit_values, _ = curve_fit(model, x_data_column, y_data, maxfev=10**6)
y_fit_model = model(x_data_column, *fit_values)
res_model = y_data - y_fit_model
# make residues from PLS
pls = PLS(n_components=3)
pls_model = pls.fit(x_data, y_data)
y_fit_pls = pls.predict(x_data)
print(y_fit_pls)
print(y_data)
print(type(y_data), type(y_fit_pls), type(y_fit_model))
print(y_data)
print(y_data.to_numpy())
print(y_fit_pls.T[0])
res_pls = y_data - y_fit_pls.T[0]
print(res_model)
print(type(res_model))
print(type(res_pls))
plt.hist(res_model,
alpha=0.5,
crossval = KFold(n_splits=5)
cv_2 = crossval.split(data_norm, resp)
cv_accuracy = np.ndarray(5)
models = list()
vip = np.ndarray((5, 19))
iter = 0
for (train, test) in cv_2:
train = np.array(train)
test = np.array(test)
train_data = data_norm[train, :]
train_resp = resp[train, :]
test_data = data_norm[test, :]
test_resp = resp[test, :]
models.append(PLS().fit(train_data, train_resp))
pls_comps = models.__getitem__(iter).transform(train_data)
testing = models.__getitem__(iter).predict(test_data)
tmp = np.sqrt(np.mean((testing - resp[test])**2))
cv_accuracy[iter] = tmp
#print(testing)
#print(resp[test])
#print(tmp)
vip[iter, :] = getVIP(models.__getitem__(iter))
#print(vip)
iter = iter + 1
for column in data.columns:
if 'nm' in column:
data_columns.append(column)
print(data_columns)
# x_data = data[data_columns]
y_columns = [
'Total Chlorophyll (ug/ml)', 'Chlorophyll a (ug/ml)',
'Chlorophyll b (ug/ml)'
]
invert_y = False
# x_data = np.log(x_data)
conditions = "Partial Least Squared"
modeler = PLS(n_components=1)
modeler_name = "Partial Least Squared"
modeler = TransformedTargetRegressor(regressor=PLS(n_components=3),
func=np.reciprocal,
inverse_func=np.reciprocal)
def plot_learning_curve(estimator,
axis,
X,
y,
ylim=None,
cv=None,
group=None,
scorer=make_scorer(mean_absolute_error),
pc1 = tmp[:,i]
pc2 = tmp[:,j]
plt.scatter(pc1, pc2)
plt.xlabel("PLS Component "+str(i+1))
plt.ylabel("PLS Component "+str(j+1))
plt.show()
##################### MAIN CODE #####################
#### Load data into numpy array'
# Keep pandas just for conveinience right now
data = load_data.loadDataPandas('../data/SCLC_study_output_filtered_2.csv')
d = data.to_numpy()
var_index = data.columns.values.tolist()
# vector of class responses associated with data
resp = load_data.getResponseMatrix2D()
#### Create object to normalize and un-normalize data
norm_trans = pre.StandardScaler().fit(d)
data_norm = norm_trans.transform(d)
#data_norm, norm_trans = pre.mean_center(d)
#In-built preprocessing method - TBD
#### Fit a Partial Least Squaresn
pls = PLS().fit(data_norm, resp)
pls_trans = pls.transform(data_norm)
plotProjectionScatterMultiClass(pls_trans, resp, 2)
# "Ridge alpha 0.01", "Ridge alpha 0.001",
# "Ridge alpha 0.0001", "Ridge alpha 0.00001",
# "Lars", "Guassian Regression",
# "Gradient Boosting",
# "SVR", "LinearSVR", "NuSVR",
# "LogisticRegression", "LinearRegression", "SGDRegressor",
# "ElasticNet", "ARDRegression", "BayesianRidge",
# "HuberRegressor", "RANSACRegressor", "TheilSenRegressor",
# "PassiveAggressiveRegressor",
# "AdaBoostRegressor", "BaggingRegressor", "GradientBoostingRegressor",
# "RandomForestRegressor", "ExtraTreesRegressor",
# "Kernel Ridge"
# ]
models_to_test = [
PLS(n_components=1),
PLS(n_components=2),
PLS(n_components=3),
PLS(n_components=4),
Lasso(alpha=5),
Lasso(alpha=2),
Lasso(alpha=1),
Lasso(alpha=0.2),
LassoLars(alpha=1),
LassoLars(alpha=0.1),
LassoLars(alpha=0.01),
LassoLars(alpha=0.001),
LassoLars(alpha=0.0003),
Ridge(alpha=0.01, max_iter=5000),
Ridge(alpha=0.001, max_iter=5000),
Ridge(alpha=0.0001, max_iter=5000),
