Skip to content
Snippets Groups Projects
Commit 03b8fde1 authored by jags's avatar jags
Browse files

Merge branch 'main' of https://git.soton.ac.uk/jagsch/comp3217-lab2 into main

parents bc2217a4 b4f01715
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
3217-classification-lr-example1.py deleted 100644 → 0
+ 0
− 43
View file @ bc2217a4
#Taken from Scikit
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
# import some data from a predefined datatset
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
#print shape of the array for X and Y. Also get value of targets
print (X.shape)
print (Y)
print (Y.shape)
# Create an instance of Logistic Regression Classifier and fit the data.
logreg = LogisticRegression(C=1)
logreg.fit(X, Y)
_, ax = plt.subplots(figsize=(4, 3))
DecisionBoundaryDisplay.from_estimator(
logreg,
X,
cmap=plt.cm.Paired,
ax=ax,
response_method="auto",
plot_method="pcolormesh",
shading="auto",
xlabel="Sepal length",
ylabel="Sepal width",
eps=0.5,
)
# Plot the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)
plt.xticks(())
plt.yticks(())
plt.show()
3217-classification-lr-example2.py deleted 100644 → 0
+ 0
− 44
View file @ bc2217a4
from sklearn import datasets, neighbors, linear_model
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, f1_score
import matplotlib.pyplot as plt
X_digits, y_digits = datasets.load_digits(return_X_y=True)
X_digits = X_digits / X_digits.max()
n_samples = len(X_digits)
ratio = 0.9
print (n_samples)
#train data
X_train = X_digits[: int(ratio * n_samples)]
y_train = y_digits[: int(ratio * n_samples)]
print (X_train.shape)
#test data
X_test = X_digits[int(ratio * n_samples) :]
y_test = y_digits[int(ratio * n_samples) :]
print (X_test.shape)
logistic = linear_model.LogisticRegression(max_iter=1000)
print(
"LogisticRegression score: %f"
% logistic.fit(X_train, y_train).score(X_test, y_test))
#Get results on actual test labels and predicted labels
predictions = logistic.predict(X_test)
#print (predictions)
#print (y_test)
#get f1 score
print (f1_score(y_test, predictions, average='macro'))
#get confusion matrix
cm = confusion_matrix(y_test, predictions, labels=logistic.classes_)
disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=logistic.classes_)
disp.plot()
plt.show()
3217-classification-lr-example3.py deleted 100644 → 0
+ 0
− 75
View file @ bc2217a4
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler
# Define a pipeline to search for the best combination of PCA truncation
# and classifier regularization.
pca = PCA()
# Define a Standard Scaler to normalize inputs
scaler = StandardScaler()
# set the tolerance to a large value to make the example faster
logistic = LogisticRegression(max_iter=10000, tol=0.1)
pipe = Pipeline(steps=[("scaler", scaler), ("pca", pca), ("logistic", logistic)])
X_digits, y_digits = datasets.load_digits(return_X_y=True)
print (X_digits.shape)
print (y_digits.shape)
# Parameters of pipelines can be set using '__' separated parameter names:
param_grid = {
"pca__n_components": [5, 15, 30, 45, 60],
"logistic__C": np.logspace(-1, 1, 1),
}
search = GridSearchCV(pipe, param_grid, n_jobs=2,cv=5)
search.fit(X_digits, y_digits)
print("Best parameter (CV score=%0.3f):" % search.best_score_)
print(search.best_params_)
# Plot the PCA spectrum
pca.fit(X_digits)
fig, (ax0, ax1) = plt.subplots(nrows=2, sharex=True, figsize=(6, 6))
ax0.plot(
np.arange(1, pca.n_components_ + 1), pca.explained_variance_ratio_, "+", linewidth=2
)
ax0.set_ylabel("PCA explained variance ratio")
ax0.axvline(
search.best_estimator_.named_steps["pca"].n_components,
linestyle=":",
label="n_components chosen",
)
ax0.legend(prop=dict(size=12))
# For each number of components, find the best classifier results
results = pd.DataFrame(search.cv_results_)
print (results)
components_col = "param_pca__n_components"
best_clfs = results.groupby(components_col).apply(
lambda g: g.nlargest(1, "mean_test_score")
)
best_clfs.plot(
x=components_col, y="mean_test_score", yerr="std_test_score", legend=False, ax=ax1
)
ax1.set_ylabel("Classification accuracy (val)")
ax1.set_xlabel("n_components")
plt.xlim(-1, 70)
plt.tight_layout()
plt.show()
3217-classification-lr-example4.py deleted 100644 → 0
+ 0
− 59
View file @ bc2217a4
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
# Load the diabetes dataset
diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
print (diabetes_X.shape)
# Use only one feature
feature_to_use = 2
diabetes_X = diabetes_X[:, np.newaxis, feature_to_use]
print (diabetes_X.shape)
test_samples = 20
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-test_samples]
diabetes_X_test = diabetes_X[-test_samples:]
# Split the targets into training/testing sets
diabetes_y_train = diabetes_y[:-test_samples]
diabetes_y_test = diabetes_y[-test_samples:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
# Make prediction using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)
print (diabetes_y_train.shape)
print (diabetes_y_test.shape)
# The coefficients
print("Coefficients: \n", regr.coef_)
# The mean squared error
print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred))
# The coefficient of determination: 1 is perfect prediction
print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred))
# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color="black") #grond truth actual test labels
plt.scatter(diabetes_X_test, diabetes_y_pred, color="red") #predicted test labels
plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)#predicted test labels
plt.xticks(())
plt.yticks(())
plt.show()
3217-classification-lr-example5.py deleted 100644 → 0
+ 0
− 34
View file @ bc2217a4
#Import scikit-learn dataset library
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm, metrics
#Load dataset
cancer = datasets.load_breast_cancer()
# print the names of the features
print("Features: ", cancer.feature_names)
# print the label type of cancer('malignant' 'benign')
print("Labels: ", cancer.target_names)
# print data(feature)shape
print (cancer.data.shape)
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2) # 70% training and 30% test
#Create a svm Classifier
clf = svm.SVC(kernel='linear') # Linear Kernel
#Train the model using the training sets
clf.fit(X_train, y_train)
#Predict the response for test dataset
y_pred = clf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment