In the workshop for this week, you are to select a data set from the UCI Machine Learning Repository and based on the recommended analysis type, wrangle the data into a fitted model, showing some model evaluation. In particular:
When complete, I will review your code, so please submit your code via pull-request to the Introduction to Machine Learning with Scikit-Learn repository!
Downloaded from the UCI Machine Learning Repository on April 16, 2016. The first thing is to fully describe your data in a README file. The dataset description is as follows:
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of interview. The problem is to predict the current contraceptive method choice (no use, long-term methods, or short-term methods) of a woman based on her demographic and socio-economic characteristics.
The data set can be used for the tasks of classification.
Lim, T.-S., Loh, W.-Y. & Shih, Y.-S. (1999). A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms. Machine Learning. ([Web Link] or [Web Link]) [Web Link]
In this section we will begin to explore the dataset to determine relevant information.
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%matplotlib inline
import os
import json
import time
import pickle
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/cmc/cmc.data"
def fetch_data(fname='cmc.txt'):
"""
Helper method to retreive the ML Repository dataset.
"""
response = requests.get(URL)
outpath = os.path.abspath(fname)
with open(outpath, 'w') as f:
f.write(response.content)
return outpath
# Fetch the data if required
DATA = fetch_data()
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FEATURES = [
"wife_age",
"wife_education",
"husband_education",
"number_births",
"wife_religion",
"wife_working",
"husband_occupation",
"sol_living",
"media_exposure",
"method_used"
]
LABEL_MAP = {
1: "No-use",
2: "Long-term",
3: "Short-term",
}
# Read the data into a DataFrame
df = pd.read_csv(DATA, sep=',', header=None, names=FEATURES)
#Convert class labels into text
for k,v in LABEL_MAP.items():
df.ix[df.method_used == k, 'method_used'] = v
# Describe the dataset
print df.head()
print df.describe()
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# Determine the shape of the data
print "{} instances with {} features\n".format(*df.shape)
# Determine the frequency of each class
print df.groupby('method_used')['method_used'].count()
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# Create a scatter matrix of the dataframe features
from pandas.tools.plotting import scatter_matrix
scatter_matrix(df, alpha=0.2, figsize=(12, 12), diagonal='kde')
plt.show()
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from pandas.tools.plotting import parallel_coordinates
plt.figure(figsize=(12,12))
parallel_coordinates(df, 'method_used')
plt.show()
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from pandas.tools.plotting import radviz
plt.figure(figsize=(12,12))
radviz(df, 'method_used')
plt.show()
One way that we can structure our data for easy management is to save files on disk. The Scikit-Learn datasets are already structured this way, and when loaded into a Bunch (a class imported from the datasets module of Scikit-Learn) we can expose a data API that is very familiar to how we've trained on our toy datasets in the past. A Bunch object exposes some important properties:
n_samples * n_featuresn_samplesNote: This does not preclude database storage of the data, in fact - a database can be easily extended to load the same Bunch API. Simply store the README and features in a dataset description table and load it from there. The filenames property will be redundant, but you could store a SQL statement that shows the data load.
In order to manage our data set on disk, we'll structure our data as follows:
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from sklearn.datasets.base import Bunch
DATA_DIR = os.path.abspath(os.path.join(".", "..","data", "contraception"))
# Show the contents of the data directory
for name in os.listdir(DATA_DIR):
if name.startswith("."): continue
print "- {}".format(name)
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def load_data(root=DATA_DIR):
# Construct the `Bunch` for the dataset
filenames = {
'meta': os.path.join(root, 'meta.json'),
'rdme': os.path.join(root, 'README.md'),
'data': os.path.join(root, 'cmc.txt'),
}
# Load the meta data from the meta json
with open(filenames['meta'], 'r') as f:
meta = json.load(f)
target_names = meta['target_names']
feature_names = meta['feature_names']
# Load the description from the README.
with open(filenames['rdme'], 'r') as f:
DESCR = f.read()
# Load the dataset from the text file.
dataset = np.loadtxt(filenames['data'], delimiter=',')
# Extract the target from the data
data = dataset[:, 0:-1]
target = dataset[:, -1]
# Create the bunch object
return Bunch(
data=data,
target=target,
filenames=filenames,
target_names=target_names,
feature_names=feature_names,
DESCR=DESCR
)
# Save the dataset as a variable we can use.
dataset = load_data()
print dataset.data.shape
print dataset.target.shape
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from sklearn import metrics
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
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def fit_and_evaluate(dataset, model, label, **kwargs):
"""
Because of the Scikit-Learn API, we can create a function to
do all of the fit and evaluate work on our behalf!
"""
start = time.time() # Start the clock!
scores = {'precision':[], 'recall':[], 'accuracy':[], 'f1':[]}
for train, test in KFold(dataset.data.shape[0], n_folds=12, shuffle=True):
X_train, X_test = dataset.data[train], dataset.data[test]
y_train, y_test = dataset.target[train], dataset.target[test]
estimator = model(**kwargs)
estimator.fit(X_train, y_train)
expected = y_test
predicted = estimator.predict(X_test)
# Append our scores to the tracker
scores['precision'].append(metrics.precision_score(expected, predicted, average="weighted"))
scores['recall'].append(metrics.recall_score(expected, predicted, average="weighted"))
scores['accuracy'].append(metrics.accuracy_score(expected, predicted))
scores['f1'].append(metrics.f1_score(expected, predicted, average="weighted"))
# Report
print "Build and Validation of {} took {:0.3f} seconds".format(label, time.time()-start)
print "Validation scores are as follows:\n"
print pd.DataFrame(scores).mean()
# Write official estimator to disk
estimator = model(**kwargs)
estimator.fit(dataset.data, dataset.target)
outpath = label.lower().replace(" ", "-") + ".pickle"
with open(outpath, 'w') as f:
pickle.dump(estimator, f)
print "\nFitted model written to:\n{}".format(os.path.abspath(outpath))
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# Perform SVC Classification
fit_and_evaluate(dataset, SVC, "Contraception Method Classifier")
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# Perform kNN Classification
fit_and_evaluate(dataset, KNeighborsClassifier, "Contraception Method kNN Classifier", n_neighbors=12)
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# Perform Random Forest Classification
fit_and_evaluate(dataset, RandomForestClassifier, "Contraception Method Random Forest Classifier")
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