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 LogisticRegressionCV
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 = True
engineering = True
N_CLASSES = 2
submission_filename = "../submissions/submission_LogisticRegressionCV.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,
engineering = engineering)
In [4]:
StatifiedCV = StratifiedKFold(y = y_train,
n_folds = 10,
shuffle = True,
random_state = SEED)
In [5]:
%%time
random.seed(SEED)
clf = LogisticRegressionCV(Cs = 10,
cv = 10,
fit_intercept = True,
penalty = 'l2',
solver = 'liblinear',
max_iter = 100,
dual = False,
scoring = None,
tol = 0.0001,
class_weight = None,
n_jobs = -1,
verbose = 0,
refit = True,
intercept_scaling = 1.0,
multi_class = 'ovr',
random_state = SEED)
# param_grid = dict(fit_intercept = [True, False],
# penalty = ['l1', 'l2'],
# #solver = ['newton-cg', 'lbfgs', 'liblinear', 'sag'],
# max_iter = [50, 100, 250])
# grid_clf = GridSearchCV(estimator = clf,
# param_grid = param_grid,
# n_jobs = 1,
# cv = StatifiedCV).fit(X_train, y_train)
# print("clf_params = {}".format(grid_clf.best_params_))
# print("score: {}".format(grid_clf.best_score_))
# clf = grid_clf.best_estimator_
clf_params = {'penalty': 'l2', 'max_iter': 50, 'fit_intercept': True}
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 = 10,
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
In [9]:
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))
In [10]:
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()
In [11]:
%%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)
In [12]:
# 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)
Out[13]:
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,
engineering = engineering)
In [15]:
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 [20]:
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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