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 August 24, 2016. The first thing is to fully describe your data in a README file. The dataset description is as follows:
The dataset contains cases from a study that was conducted between 1958 and 1970 at the University of Chicago's Billings Hospital on the survival of patients who had undergone surgery for breast cancer.
The data set can be used for the tasks of classification and cluster analysis.
Below are the attributes:
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
In [2]:
URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data"
def fetch_data(fname='habeerman.data'):
"""
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()
print(DATA)
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FEATURES = [
"Age",
"year",
"nodes",
"label"
]
LABEL_MAP = {
1: "Survived",
2: "Died",
}
# Read the data into a DataFrame
df = pd.read_csv(DATA, header=None, names=FEATURES)
print(df.head())
# Convert class labels into text
for k,v in LABEL_MAP.items():
df.ix[df.label == k, 'label'] = v
print(df.label.unique())
#check label values
print(df.head())
# Describe the dataset
print df.describe()
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# Determine the shape of the data
print "{} instances with {} features\n".format(*df.shape)
#I believe the shape includes the lables.
# Determine the frequency of each class
print df.groupby('label')['label'].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, 'label')
plt.show()
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from pandas.tools.plotting import radviz
plt.figure(figsize=(12,12))
radviz(df, 'label')
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",'habeerman'))
#C:\Users\pbw50\machine-learning\notebook\habeerman.data
# 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 habeerman dataset
filenames = {
'meta': os.path.join(root, 'meta.json'),
'rdme': os.path.join(root, 'README.md'),
'data': os.path.join(root, 'habeerman.data'),
}
#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 data file.
dataset = pd.read_csv(filenames['data'], header=None)
#tranform to numpy
data1 = dataset.ix[:, 0:2]
target1 = dataset.ix[:,3]
# Extract the target from the data
data = np.array(data1)
target = np.array(target1)
# 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="binary"))
scores['recall'].append(metrics.recall_score(expected, predicted, average="binary"))
scores['accuracy'].append(metrics.accuracy_score(expected, predicted))
scores['f1'].append(metrics.f1_score(expected, predicted, average="binary"))
# 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, "habeerman SVM Classifier", )
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# Perform kNN Classification
fit_and_evaluate(dataset, KNeighborsClassifier, "habeerman kNN Classifier", n_neighbors=12)
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# Perform Random Forest Classification
fit_and_evaluate(dataset, RandomForestClassifier, "habeerman Random Forest Classifier")
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