In this example, I make use of the previous routines and apply basic machine learning tasks to a real dataset.
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In [1]:
#This routine to plot learning curve is from sklearn documentation.
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the test and training learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is not a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validators that can be used here.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
"""
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
return plt
Now we work on the Adult UCL dataset. This is a popular dataset with the following data about a collection of adults:
age: continuous.
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, ithout-pay, Never-worked.
fnlwgt: continuous.
education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
education-num: continuous.
marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
sex: Female, Male.
capital-gain: continuous.
capital-loss: continuous.
hours-per-week: continuous.
native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
class: >50K, <=50K
In [2]:
#Import data from files
import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases/adult/adult.data', header=None).head(2000)
df.columns = ["age","workclass","fnlwgt","education","education-num","marital-status","occupation",
"relationship","race","sex","capital-gain","capital-loss","hours-per-week","native-country","class"]
#nfeature=len(df.columns)
#print("Number of features: "+str(nfeature-1))
#nfeature=14
df.head(5)
df.shape
Out[2]:
Here we start using concepts of machine learning.
In [3]:
from sklearn_pandas import DataFrameMapper, cross_val_score
import pandas as pd
import numpy as np
import sklearn.preprocessing, sklearn.decomposition, sklearn.linear_model, sklearn.pipeline, sklearn.metrics
from sklearn.feature_extraction.text import CountVectorizer
#Target column
# select 'class' column. If <=50K set to 1 and > 50K to 0
df['class'] = df['class'].str.strip()
y = df.loc[:,'class']
y = np.where(y == '<=50K' , 0, 1)
#Split dataframe into train and test datasets (partition = 80 and 20%):
msk = np.random.rand(len(df)) < 0.8
y_train = y[msk]
y_test = y[~msk]
print("Training set with %d entries\n" % (y_train.size))
print("Test set with %d entries\n" % (y_test.size))
mapper_df = DataFrameMapper([
(['age'], sklearn.preprocessing.StandardScaler()),
('workclass', sklearn.preprocessing.LabelBinarizer()),
(['fnlwgt'],sklearn.preprocessing.StandardScaler()),
('education',sklearn.preprocessing.LabelBinarizer()),
('marital-status',sklearn.preprocessing.LabelBinarizer()),
('occupation',sklearn.preprocessing.LabelBinarizer()),
('native-country',sklearn.preprocessing.LabelBinarizer()),
('native-country',sklearn.preprocessing.LabelBinarizer()),
('sex',sklearn.preprocessing.LabelBinarizer()),
(['hours-per-week'], sklearn.preprocessing.StandardScaler()),
], df_out=True)
#
df_vectorized=np.round(mapper_df.fit_transform(df.copy()), 2)
df_vectorized.head()
features=df_vectorized.columns
#Convert to np.matrix again:
X=df_vectorized.as_matrix()
See previous example ipynb or read sklearn documentation.
In [4]:
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier
# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
random_state=0)
forest.fit(X, y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
#Retain only few features
nfeatures=6
# Print the feature ranking (top ten only)
print("Feature ranking:")
for f in range(nfeatures):
print("%d. feature %d (%20s): %f" %
(f + 1, indices[f],features[indices[f]], importances[indices[f]]))
# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
#print(X.shape[1])
plt.bar(range(nfeatures), importances[indices[:nfeatures]],
color="r", yerr=std[indices[:nfeatures]], align="center")
plt.xticks(range(nfeatures), indices[:nfeatures])
plt.xlim([-1, nfeatures])
plt.show()
We find that most features are not correlated with the class: Lets make a logistic regression using the top ten features above:
In [5]:
#Routine to plot learning curve from sklearn
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
from sklearn import svm, datasets
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the test and training learning curve.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
title : string
Title for the chart.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
ylim : tuple, shape (ymin, ymax), optional
Defines minimum and maximum yvalues plotted.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is not a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validators that can be used here.
n_jobs : integer, optional
Number of jobs to run in parallel (default 1).
"""
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
return plt
In [6]:
from sklearn import svm
#Use the feature ranking to reduce the dimensionality of the dataset:
indx_list=(np.array(indices[:nfeatures],dtype=np.int))
X=df_vectorized.iloc[:,indx_list]
X_train=X[msk]
X_test=X[~msk]
title = "Learning Curves (Linear SVC)"
cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
estimator = svm.LinearSVC(C=1.0)
plot_learning_curve(estimator, title, X_train, y_train, (0.7, 1.01), cv=cv, n_jobs=4)
plt.show()
Use sklearn to plot the precision score. This time, I use the test dataset.
The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0.
In [7]:
from sklearn.metrics import classification_report
estimator.fit(X_train, y_train)
y_pred = estimator.predict(X_test)
ntest=y_pred.size
#Total of true positives:
from sklearn.metrics import precision_score
#Precision score for test dataset:
print("Precision score for test dataset: \n")
precision_score(y_test, y_pred, average='micro')
Out[7]:
In [8]:
# Import
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
X_train_poly = poly.fit_transform(X_train)
X_test_poly=poly.fit_transform(X_test)
In [10]:
#estimator = svm.LinearSVC(C=1.0)
estimator.fit(X_train_poly, y_train)
y_pred = estimator.predict(X_test_poly)
#Precision score for test dataset:
print("Precision score for test dataset: \n")
precision_score(y_test, y_pred, average='micro')
Out[10]:
Summarizing, here we used Pandas to read a dataset. Then we vectorized non-numeric features. Our set had a large number of features, so I decided to keep the most relevant features found by a feature-selection algorithm. We then used a linear classification model with a precision of ~0.79. We tried to improve the precision using a polynomial model of higher degree but the precision was marginally improved to 0.8.
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