def model(timestamps,
predictors,
classes,
classifier=None,
prediction_attribute='predict_proba',
hyperparams=None,
roc_bounds=None,
verbose=False):
'''
Creates several models using leave-one-year-out cross validation.
ROC and PR curves are plotted as a side-effect.
Parameters
----------
timestamps : Nx1 pandas series of timestamps.
Each element should have a "year" attribute.
predictors : NxM pandas DataFrame, all values should be numeric,
and there should be no NaN values.
classes : Nx1 array like of binary outcomes, e.g. True or False.
classifier : sklearn classifier, should have the attributes "fit"
and "predict_proba" at the least.
hyperparams: Dictionary of hyper parameters to pass to the
classifier method.
prediction_attribute:
Name of the attribute of the classifier that returns
the probability of belonging to the positive class.
roc_bounds : [min, max] values to use when computing partial AUC.
verbose : True if the clf.feature_importances_ should be printed
Returns
-------
clfs : Dictionary of (year, classifier) pairs, where the classifier
is the model found by leaving the specified year out of the
training set.
roc_ax : the matplotlib axes object containing all of the ROC curves.
pr_ax : the matplotlib axes object containing all of the PR curves.
'''
if classifier is None:
classifier = sklearn.ensemble.GradientBoostingClassifier
if hyperparams is None:
hyperparams = {}
timestamps = timestamps.map(lambda x: x.year)
start = timestamps.min()
stop = timestamps.max()
stop = min(stop, 2014) # do not include 2015
roc_fig, roc_ax = plt.subplots(1, figsize=[12, 9])
pr_fig, pr_ax = plt.subplots(1, figsize=[12, 9])
roc_fig.subplots_adjust(left=0.07, right=0.67)
pr_fig.subplots_adjust(left=0.07, right=0.67)
clfs = dict()
auc_rocs = []
for yr in range(start, stop + 1):
is_not_yr = timestamps != yr
train_indices = np.array(is_not_yr)
test_indices = np.array(~is_not_yr)
clf = classifier(**hyperparams)
clf.fit(predictors.ix[train_indices, :], classes[train_indices])
clfs[yr] = clf
predictions = getattr(clf, prediction_attribute)(
predictors.ix[test_indices, :])[:, 1]
auc_roc = viz.roc(predictions,
classes[test_indices],
block_show=False,
ax=roc_ax,
bounds=roc_bounds)[3]
auc_pr = viz.precision_recall(predictions,
classes[test_indices],
block_show=False,
ax=pr_ax)[3]
auc_roc = float(auc_roc)
auc_rocs.append(auc_roc)
auc_pr = float(auc_pr)
roc_ax.get_lines()[-2].set_label(
str(yr) + ' - AUC: {0:.5f}'.format(auc_roc))
pr_ax.get_lines()[-2].set_label(
str(yr) + ' - AUC: {0:.5f}'.format(auc_pr))
if verbose:
print('Year ' + str(yr))
print('Feature importances:')
feat_imps = clf.feature_importances_
idxs = np.argsort(feat_imps)[::-1]
max_width = max([len(c) for c in predictors.columns])
for c, fi in zip(predictors.columns[idxs], feat_imps[idxs]):
print(' {0:<{1}} : {2:.5f}'.format(c, max_width + 1, fi))
return clfs, auc_rocs, roc_ax, pr_ax
'auto') # we are going to be zooming around, set it to auto
roc_ax.grid(True, which='major')
roc_ax.set_title('ROC - mean pAUC: {0:.7f} $\pm$ {1:.7f}'.format(
np.mean(auc_rocs), np.std(auc_rocs)))
roc_ax.set_ylim([0, .5])
## Precision-Recall curve
### Make PR yearly lines more transparent
c = pr_ax.get_children()
for line in c:
line.set_alpha(.7)
### Plot PR curve for EPA model
tpr, ppv, threshes, auc_pr = viz.precision_recall(
epa_model_df['Drek_Prediction'],
epa_model_df['Escherichia.coli'] > 235,
ax=pr_ax,
block_show=False)
### Format the EPA line
auc_pr = float(auc_pr)
epa_line = pr_ax.get_lines()[-2]
epa_line.set_color([0, 0, 0])
epa_line.set_ls('--')
epa_line.set_linewidth(3)
epa_line.set_alpha(.85)
epa_line.set_label('EPA Model - AUC: {0:.5f}'.format(auc_pr))
### Plot an X where the current model is performing
i = np.where(threshes < 235.0)[0][0]
pr_ax.plot(tpr[i],
ppv[i],
ax=roc_ax, block_show=False)[3]
auc_roc = float(auc_roc)
epa_line = roc_ax.get_lines()[-2]
epa_line.set_color([0,0,0])
epa_line.set_ls('--')
epa_line.set_linewidth(3)
epa_line.set_alpha(.85)
epa_line.set_label('EPA Model - AUC: {0:.4f}'.format(auc_roc))
roc_ax.legend(loc=4)
roc_ax.grid(True, which='major')
c = pr_ax.get_children()
for line in c:
line.set_alpha(.75)
auc_pr = viz.precision_recall(epa_model_df['Drek_Prediction'],
epa_model_df['Escherichia.coli'] > 235,
ax=pr_ax, block_show=False)[3]
auc_pr = float(auc_pr)
epa_line = pr_ax.get_lines()[-2]
epa_line.set_color([0,0,0])
epa_line.set_ls('--')
epa_line.set_linewidth(3)
epa_line.set_alpha(.85)
epa_line.set_label('EPA Model - AUC: {0:.4f}'.format(auc_pr))
pr_ax.legend(loc=1)
pr_ax.grid(True, which='major')
plt.draw()
plt.show(block=True)
def model(timestamps, predictors, classes, classifier=None, hyperparams=None, verbose=False):
'''
Creates several GBMs using leave-one-year-out cross validation.
ROC and PR curves are plotted as a side-effect.
Parameters
----------
timestamps : Nx1 pandas series of timestamps.
Each element should have a "year" attribute.
predictors : NxM pandas DataFrame, all values should be numeric,
and there should be no NaN values.
classes : Nx1 array like of binary outcomes, e.g. True or False.
classifier : sklearn classifier, should have the attributes "fit"
and "predict_proba" at the least.
hyperparams: Dictionary of hyper parameters to pass to the
classifier method.
verbose : True if the clf.feature_importances_ should be printed
Returns
-------
clfs : Dictionary of (year, classifier) pairs, where the classifier
is the model found by leaving the specified year out of the
training set.
'''
if classifier is None:
classifier = sklearn.ensemble.GradientBoostingClassifier
if hyperparams is None:
hyperparams = {}
timestamps = timestamps.map(lambda x: x.year)
start = timestamps.min()
stop = timestamps.max()
stop = min(stop, 2014) # do not include 2015
roc_ax = plt.subplots(1)[1]
pr_ax = plt.subplots(1)[1]
clfs = dict()
for yr in range(start, stop+1):
train_indices = np.array((timestamps < yr) | (timestamps > yr))
clf = classifier(**hyperparams)
clf.fit(predictors.ix[train_indices,:], classes[train_indices])
clfs[yr] = clf
predictions = clf.predict_proba(predictors.ix[~train_indices,:])[:,1]
auc_roc = viz.roc(predictions, classes[~train_indices],
block_show=False, ax=roc_ax)[3]
auc_pr = viz.precision_recall(predictions, classes[~train_indices],
block_show=False, ax=pr_ax)[3]
auc_roc = float(auc_roc)
auc_pr = float(auc_pr)
roc_ax.get_lines()[-2].set_label(str(yr) + ' - AUC: {0:.4f}'.format(auc_roc))
pr_ax.get_lines()[-2].set_label(str(yr) + ' - AUC: {0:.4f}'.format(auc_pr))
if verbose:
print('Year ' + str(yr))
print('Feature importances:')
feat_imps = clf.feature_importances_
idxs = np.argsort(feat_imps)[::-1]
max_width = max([len(c) for c in predictors.columns])
for c, fi in zip(predictors.columns[idxs], feat_imps[idxs]):
print(' {0:<{1}} : {2:.5f}'.format(c, max_width+1, fi))
return clfs, roc_ax, pr_ax
def model(timestamps, predictors, classes,
classifier=None,
prediction_attribute='predict_proba',
hyperparams=None,
roc_bounds=None,
verbose=False):
'''
Creates several models using leave-one-year-out cross validation.
ROC and PR curves are plotted as a side-effect.
Parameters
----------
timestamps : Nx1 pandas series of timestamps.
Each element should have a "year" attribute.
predictors : NxM pandas DataFrame, all values should be numeric,
and there should be no NaN values.
classes : Nx1 array like of binary outcomes, e.g. True or False.
classifier : sklearn classifier, should have the attributes "fit"
and "predict_proba" at the least.
hyperparams: Dictionary of hyper parameters to pass to the
classifier method.
prediction_attribute:
Name of the attribute of the classifier that returns
the probability of belonging to the positive class.
roc_bounds : [min, max] values to use when computing partial AUC.
verbose : True if the clf.feature_importances_ should be printed
Returns
-------
clfs : Dictionary of (year, classifier) pairs, where the classifier
is the model found by leaving the specified year out of the
training set.
roc_ax : the matplotlib axes object containing all of the ROC curves.
pr_ax : the matplotlib axes object containing all of the PR curves.
'''
if classifier is None:
classifier = sklearn.ensemble.GradientBoostingClassifier
if hyperparams is None:
hyperparams = {}
timestamps = timestamps.map(lambda x: x.year)
start = timestamps.min()
stop = timestamps.max()
stop = min(stop, 2014) # do not include 2015
roc_fig, roc_ax = plt.subplots(1, figsize=[12, 9])
pr_fig, pr_ax = plt.subplots(1, figsize=[12, 9])
roc_fig.subplots_adjust(left=0.07, right=0.67)
pr_fig.subplots_adjust(left=0.07, right=0.67)
clfs = dict()
auc_rocs = []
for yr in range(start, stop+1):
is_not_yr = timestamps != yr
train_indices = np.array(is_not_yr)
test_indices = np.array(~is_not_yr)
clf = classifier(**hyperparams)
clf.fit(predictors.ix[train_indices,:], classes[train_indices])
clfs[yr] = clf
predictions = getattr(clf, prediction_attribute)(predictors.ix[test_indices,:])[:,1]
auc_roc = viz.roc(predictions,
classes[test_indices],
block_show=False,
ax=roc_ax,
bounds=roc_bounds)[3]
auc_pr = viz.precision_recall(predictions,
classes[test_indices],
block_show=False,
ax=pr_ax)[3]
auc_roc = float(auc_roc)
auc_rocs.append(auc_roc)
auc_pr = float(auc_pr)
roc_ax.get_lines()[-2].set_label(str(yr) + ' - AUC: {0:.5f}'.format(auc_roc))
pr_ax.get_lines()[-2].set_label(str(yr) + ' - AUC: {0:.5f}'.format(auc_pr))
if verbose:
print('Year ' + str(yr))
print('Feature importances:')
feat_imps = clf.feature_importances_
idxs = np.argsort(feat_imps)[::-1]
max_width = max([len(c) for c in predictors.columns])
for c, fi in zip(predictors.columns[idxs], feat_imps[idxs]):
print(' {0:<{1}} : {2:.5f}'.format(c, max_width+1, fi))
return clfs, auc_rocs, roc_ax, pr_ax
