Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
I talked about learningmachine – a package for machine learning with uncertainty quantification and interpretatbility – last week in #131.
Here comes the Python version!
Keep in mind that learningmachine is still experimental, probably with some quirks (because achieving this level of abstraction required some effort), with no beautiful documentation, but you can already tinker it and do advanced analysis, as shown below.
0 - Install and load packages
%load_ext rpy2.ipython
%%R
utils::install.packages("c('remotes', 'ranger')")
remotes::install_github("Techtonique/learningmachine")
!pip install learningmachine --upgrade --no-cache-dir
import learningmachine as lm
import numpy as np
from sklearn.datasets import load_breast_cancer, load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from time import time
1 - Adjust classifiers
clf_list = []
fit_obj = lm.Classifier(method = "ranger", level=None, nb_hidden=None)
dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=13)
start = time()
fit_obj.fit(X_train, y_train)
print("Elapsed time: ", time() - start)
preds = fit_obj.predict(X_test)
score = np.mean(preds.ravel().astype(int) == y_test)
clf_list.append((fit_obj, "ranger", fit_obj.predict_proba(X_test), score))
print(classification_report(y_test, preds.ravel().astype(int)))
Elapsed time: 2.2424495220184326
precision recall f1-score support
0 0.83 0.94 0.88 36
1 0.97 0.91 0.94 78
accuracy 0.92 114
macro avg 0.90 0.93 0.91 114
weighted avg 0.93 0.92 0.92 114
fit_obj = lm.Classifier(method = "ranger", level=None, nb_hidden=25)
dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=13)
start = time()
fit_obj.fit(X_train, y_train)
print("Elapsed time: ", time() - start)
score = np.mean(fit_obj.predict(X_test).ravel().astype(int) == y_test)
clf_list.append((fit_obj, "ranger_qrnn", fit_obj.predict_proba(X_test), score))
print(classification_report(y_test, preds.ravel().astype(int)))
Elapsed time: 0.4195363521575928
precision recall f1-score support
0 0.83 0.94 0.88 36
1 0.97 0.91 0.94 78
accuracy 0.92 114
macro avg 0.90 0.93 0.91 114
weighted avg 0.93 0.92 0.92 114
fit_obj = lm.Classifier(method = "ranger", level=95, nb_hidden=0)
dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=13)
start = time()
fit_obj.fit(X_train, y_train)
print("Elapsed time: ", time() - start)
preds = fit_obj.predict(X_test)
score = np.mean(preds.ravel().astype(int) == y_test)
clf_list.append((fit_obj, "ranger_calibrated", fit_obj.predict_proba(X_test), score))
print(classification_report(y_test, preds.ravel().astype(int)))
Elapsed time: 0.23552465438842773
precision recall f1-score support
0 0.88 0.97 0.92 36
1 0.99 0.94 0.96 78
accuracy 0.95 114
macro avg 0.93 0.95 0.94 114
weighted avg 0.95 0.95 0.95 114
fit_obj = lm.Classifier(method = "ranger", level=95, nb_hidden=25)
dataset = load_breast_cancer()
X = dataset.data
y = dataset.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=13)
start = time()
fit_obj.fit(X_train, y_train)
print("Elapsed time: ", time() - start)
preds = fit_obj.predict(X_test)
score = np.mean(preds.ravel().astype(int) == y_test)
clf_list.append((fit_obj, "ranger_qrnn_calibrated", fit_obj.predict_proba(X_test), score))
print(classification_report(y_test, preds.ravel().astype(int)))
Elapsed time: 0.23675990104675293
precision recall f1-score support
0 0.85 0.97 0.91 36
1 0.99 0.92 0.95 78
accuracy 0.94 114
macro avg 0.92 0.95 0.93 114
weighted avg 0.94 0.94 0.94 114
names = [clf_list[i][1] for i in range(len(clf_list))]
classifiers = [clf_list[i][0] for i in range(len(clf_list))]
scores = [clf_list[i][2] for i in range(len(clf_list))]
2 - Visualizing classifiers in 2D
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
from sklearn.datasets import make_circles, make_classification, make_moons
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
X, y = make_classification(
n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1
)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)
datasets = [
make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
linearly_separable,
]
figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds_cnt, ds in enumerate(datasets):
# preprocess dataset, split into training and test part
X, y = ds[0], ds[1]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=42
)
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(["#FF0000", "#0000FF"])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
if ds_cnt == 0:
ax.set_title("Input data")
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k")
# Plot the testing points
ax.scatter(
X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k"
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
i += 1
# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf = make_pipeline(StandardScaler(), clf)
clf.fit(X_train, y_train)
try:
score = clf.score(X_test, y_test)
except: # no scoring method available yet for prediction sets
score = np.mean(clf.predict_proba(X_test).argmax(axis=1) == y_test)
DecisionBoundaryDisplay.from_estimator(
clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5
)
# Plot the training points
ax.scatter(
X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k"
)
# Plot the testing points
ax.scatter(
X_test[:, 0],
X_test[:, 1],
c=y_test,
cmap=cm_bright,
edgecolors="k",
alpha=0.6,
)
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
if ds_cnt == 0:
ax.set_title(name)
ax.text(
x_max - 0.3,
y_min + 0.3,
("%.2f" % score).lstrip("0"),
size=15,
horizontalalignment="right",
)
i += 1
plt.tight_layout()
plt.show()
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