In [1]:
from __future__ import division
from IPython.display import display
from matplotlib import pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import random, sys, os, re
from sklearn.linear_model import SGDClassifier
from sklearn.cross_validation import StratifiedKFold
from sklearn.grid_search import RandomizedSearchCV, GridSearchCV
from sklearn.cross_validation import cross_val_predict, permutation_test_score
In [2]:
SEED = 97
scale = False
minmax = False
norm = False
nointercept = False
N_CLASSES = 2
submission_filename = "../submissions/submission_SGDClassifier.csv"
In [3]:
from load_blood_data import load_blood_data
y_train, X_train = load_blood_data(train=True, SEED = SEED,
scale = scale,
minmax = minmax,
norm = norm,
nointercept = nointercept)
In [4]:
StatifiedCV = StratifiedKFold(y = y_train,
n_folds = 10,
shuffle = True,
random_state = SEED)
In [5]:
%%time
random.seed(SEED)
# NOTE: For best results using the default learning rate schedule,
# the data should have zero mean and unit variance.
clf = SGDClassifier(loss = 'hinge', # 'hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron',
# or a regression loss: 'squared_loss', 'huber', 'epsilon_insensitive',
# 'squared_epsilon_insensitive'
penalty = 'l2', # 'none', 'l2', 'l1', or 'elasticnet'
alpha = 0.0001, # multiplies a single regularization term
l1_ratio = 0.15, # Elastic Net mixing parameter
n_iter = 5, # epochs
fit_intercept = False, # If False, the data is assumed to be centered.
epsilon = 0.1, # for 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
shuffle = True,
verbose = 0,
n_jobs = -1,
random_state = SEED,
learning_rate = 'optimal', # The learning rate schedule
eta0 = 0.0, # The initial learning rate for 'constant' or 'invscaling'
power_t = 0.5, # The exponent for inverse scaling learning rate
class_weight = None,
warm_start = False)
#average = False)
# param_grid = dict(loss = ['hinge','log','modified_huber','squared_hinge','perceptron'],
# penalty = ['none','elasticnet'],
# l1_ratio = [0.0, 0.001, 0.01, 0.1, 0.5, 1.0],
# n_iter = [5, 10, 20, 30, 40])
# grid_clf = GridSearchCV(estimator = clf,
# param_grid = param_grid,
# n_jobs = -1,
# cv = StatifiedCV,
# verbose = 0
# )
# grid_clf.fit(X = X_train, y = y_train)
# print("clf_params = {}".format(grid_clf.best_params_))
# print("score: {}".format(round(grid_clf.best_score_, 4)))
# print
# clf = grid_clf.best_estimator_
clf_params = {'penalty': 'elasticnet', 'l1_ratio': 0.01, 'n_iter': 20, 'loss': 'squared_hinge'}
clf.set_params(**clf_params)
clf.fit(X_train, y_train)
In [6]:
# from sklearn_utilities import GridSearchHeatmap
# GridSearchHeatmap(grid_clf, y_key='learning_rate', x_key='n_estimators')
# from sklearn_utilities import plot_validation_curves
# plot_validation_curves(grid_clf, param_grid, X_train, y_train, ylim = (0.0, 1.05))
In [7]:
%%time
try:
from sklearn_utilities import plot_learning_curve
except:
import imp, os
util = imp.load_source('sklearn_utilities', os.path.expanduser('~/Dropbox/Python/sklearn_utilities.py'))
from sklearn_utilities import plot_learning_curve
plot_learning_curve(estimator = clf,
title = None,
X = X_train,
y = y_train,
ylim = (0.0, 1.10),
cv = StratifiedKFold(y = y_train,
n_folds = 10,
shuffle = True,
random_state = SEED),
train_sizes = np.linspace(.1, 1.0, 5),
n_jobs = -1)
plt.show()
In [8]:
%%time
train_preds = cross_val_predict(estimator = clf,
X = X_train,
y = y_train,
cv = StatifiedCV,
n_jobs = -1,
verbose = 0,
fit_params = None,
pre_dispatch = '2*n_jobs')
y_true, y_pred = y_train, train_preds
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from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_true, y_pred, labels=None)
print cm
try:
from sklearn_utilities import plot_confusion_matrix
except:
import imp, os
util = imp.load_source('sklearn_utilities', os.path.expanduser('~/Dropbox/Python/sklearn_utilities.py'))
from sklearn_utilities import plot_confusion_matrix
plot_confusion_matrix(cm, ['Did not Donate','Donated'])
accuracy = round(np.trace(cm)/float(np.sum(cm)),4)
misclass = 1 - accuracy
print("Accuracy {}, mis-class rate {}".format(accuracy,misclass))
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from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import log_loss
from sklearn.metrics import f1_score
fpr, tpr, thresholds = roc_curve(y_true, y_pred, pos_label=None)
plt.figure(figsize=(10,6))
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(fpr, tpr)
AUC = roc_auc_score(y_true, y_pred, average='macro')
plt.text(x=0.6,y=0.4,s="AUC {:.4f}"\
.format(AUC),
fontsize=16)
plt.text(x=0.6,y=0.3,s="accuracy {:.2f}%"\
.format(accuracy*100),
fontsize=16)
logloss = log_loss(y_true, y_pred)
plt.text(x=0.6,y=0.2,s="LogLoss {:.4f}"\
.format(logloss),
fontsize=16)
f1 = f1_score(y_true, y_pred)
plt.text(x=0.6,y=0.1,s="f1 {:.4f}"\
.format(f1),
fontsize=16)
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.show()
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%%time
score, permutation_scores, pvalue = permutation_test_score(estimator = clf,
X = X_train.values.astype(np.float32),
y = y_train,
cv = StatifiedCV,
labels = None,
random_state = SEED,
verbose = 0,
n_permutations = 100,
scoring = None,
n_jobs = -1)
plt.figure(figsize=(20,8))
plt.hist(permutation_scores, 20, label='Permutation scores')
ylim = plt.ylim()
plt.plot(2 * [score], ylim, '--g', linewidth=3,
label='Classification Score (pvalue {:.4f})'.format(pvalue))
plt.plot(2 * [1. / N_CLASSES], ylim, 'r', linewidth=7, label='Luck')
plt.ylim(ylim)
plt.legend(loc='center',fontsize=16)
plt.xlabel('Score')
plt.show()
# find mean and stdev of the scores
from scipy.stats import norm
mu, std = norm.fit(permutation_scores)
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# format for scores.csv file
import re
algo = re.search(r"submission_(.*?)\.csv", submission_filename).group(1)
print("{: <26} , , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f}"\
.format(algo,accuracy,logloss,AUC,f1,mu,std))
In [13]:
clf.set_params(**clf_params)
clf.fit(X_train, y_train)
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In [14]:
from load_blood_data import load_blood_data
X_test, IDs = load_blood_data(train=False, SEED = SEED,
scale = scale,
minmax = minmax,
norm = norm,
nointercept = nointercept)
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y_pred = clf.predict(X_test)
print(y_pred[:10])
try:
y_pred_probs = clf.predict_proba(X_test)
print(y_pred_probs[:10])
donate_probs = [prob[1] for prob in y_pred_probs]
except Exception,e:
print(e)
donate_probs = [0.65 if x>0 else 1-0.65 for x in y_pred]
print(donate_probs[:10])
In [164]:
assert len(IDs)==len(donate_probs)
f = open(submission_filename, "w")
f.write(",Made Donation in March 2007\n")
for ID, prob in zip(IDs, donate_probs):
f.write("{},{}\n".format(ID,prob))
f.close()
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