Machine learning basics
Machine Learning cho Dữ liệu Chuỗi thời gian bằng Python
Chris Holdgraf
Fellow, Berkeley Institute for Data Science
Always begin by looking at your data
array.shape
(10, 5)
array[:3]
array([[ 0.735528 , 1.00122818, -0.28315978],
[-0.94478393, 0.18658748, -0.00241224],
[-0.74822942, -1.46636618, 0.69835096]])
Always begin by looking at your data
df.head()
col1 col2 col3
0 0.735528 1.001228 -0.283160
1 -0.944784 0.186587 -0.002412
2 -0.748229 -1.466366 0.698351
3 1.038589 -0.171248 0.831457
4 -0.161904 0.003972 -0.321933
Always visualize your data
Make sure it looks the way you'd expect.
# Using matplotlib
fig, ax = plt.subplots()
ax.plot(...)
# Using pandas
fig, ax = plt.subplots()
df.plot(..., ax=ax)
Scikit-learn
Scikit-learn is the most popular machine learning library in Python
from sklearn.svm import LinearSVC
Preparing data for scikit-learn
scikit-learnexpects a particular structure of data:(samples, features)Make sure that your data is at least two-dimensional
Make sure the first dimension is samples
If your data is not shaped properly
- If the axes are swapped:
array.T.shape
(10, 3)
If your data is not shaped properly
- If we're missing an axis, use
.reshape():
array.shape
(10,)
array.reshape(-1, 1).shape
(10, 1)
-1will automatically fill that axis with remaining values
Fitting a model with scikit-learn
# Import a support vector classifier
from sklearn.svm import LinearSVC
# Instantiate this model
model = LinearSVC()
# Fit the model on some data
model.fit(X, y)
It is common for y to be of shape (samples, 1)
Investigating the model
# There is one coefficient per input feature
model.coef_
array([[ 0.69417875, -0.5289162 ]])
Predicting with a fit model
# Generate predictions
predictions = model.predict(X_test)
Let's practice
Machine Learning cho Dữ liệu Chuỗi thời gian bằng Python
