def _iter_cv(n): # XXX support sklearn < 0.18
if hasattr(LeaveOneOut, 'split'):
cv = LeaveOneOut()
return cv.split(np.zeros((n, 1)))
else:
cv = LeaveOneOut(len(data))
return cv
def run_leave_one_out_cv(features, labels, classifier=LinearDiscriminantAnalysis()):
"""
Runs leave one out CV.
:param features: Features shape(epoch, feature)
:param labels: list of lables of length num epochs
:param classifier: Sklearn classifier (Defaults to LDA)
:return: A list of cross validation scores. Use np.average on the result to find the average score.
"""
loo = LeaveOneOut()
scores = []
for train_indexes, test_indexes in loo.split(features, labels):
# Assert our split maintains the same number of features
CCDLAssert.assert_equal(len(train_indexes) + len(test_indexes), features.shape[0])
# Assert we have the same number of features
X_train, X_test = features[train_indexes, :], features[test_indexes, :]
Y_train, Y_test = np.asarray(labels)[train_indexes], np.asarray(labels)[test_indexes]
# Assert our X_train and X_test have the same number of features
CCDLAssert.assert_equal(X_train.shape[1], X_test.shape[1])
# Fit our classifier to our
classifier.fit(X_train, Y_train)
score = classifier.score(X_test, Y_test)
scores.append(score)
return scores
def main(argv):
filename = argv[0]
t = float(argv[1]) # threshold for logistic regression (default=0.5)
dup = int(argv[2]) # if 1, bad queries will be duplicated
subset = 'cache' # column title for precision of cache
full = 'full' # column title for precision of full db
df = pd.read_csv('../../data/cache_selection_structured/' + filename)
df = df.drop(['query', 'freq'], axis = 1)
df = df.fillna(0)
df['label'] = np.where(df['full'] > df['cache'], 1, 0)
if dup:
print('duping..')
bads = df[df['label'] == 1]
df = df.append(bads, ignore_index=True)
X = df.drop(['label'], axis = 1)
y = df['label']
df = df.drop(['label'], axis = 1)
p20_mean = np.zeros([1, 6])
bad_mean = np.zeros([1, 6])
ml_average_rare = 0
ql_average_rare = 0
best_average_rare = 0
loo = LeaveOneOut()
bad_counter = 0
for train_index, test_index in loo.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
X_train = X_train.drop([subset, full], axis=1)
p12 = X_test[subset].iloc[0]
p100 = X_test[full].iloc[0]
is_bad = p12 < p100
X_test = X_test.drop([subset, full], axis=1)
# compute query likelihood based effectiveness
ql_cache = np.mean(X_test['ql_0_0'] + X_test['ql_0_1'] +
X_test['ql_1_0'] + X_test['ql_2_0'])
ql_rest = np.mean(X_test['ql_rest_0_0'] + X_test['ql_rest_0_1'] +
X_test['ql_rest_1_0'] + X_test['ql_rest_2_0'])
#ql_pred = X_test['ql_0_1'].iloc[0] < X_test['ql_rest_0_1'].iloc[0]
ql_pred = 1 if ql_cache < ql_rest else 0
ql = p12 if ql_pred == 0 else p100
# learn the model
print(X_train.shape)
print(df.columns.shape)
# y_pred = train_lr(X_train, y_train, X_test, y_test, t, df.columns.values[:-2])
y_pred = train_lr(X_train, y_train, X_test, y_test, t)
ml = p12 if y_pred[0] == 0 else p100
best = p12 if y_test.iloc[0] == 0 else p100
rnd = p12 if np.random.randint(0, 2) == 1 else p100
p20_mean += [p12, p100, ml, ql,
best, rnd]
if is_bad:
#bad_mean += [p12[0], p100[0], ml[0], ql[0], best[0], rnd[0]]
bad_mean += [p12, p100, ml, ql, best, rnd]
bad_counter += 1
print('final results:')
print('\t'.join(map(str,['set', 'cache', 'db', 'ml', 'ql', 'best',
'rand'])))
print('\t'.join(['bad'] + map(str, np.round(bad_mean[0] / bad_counter, 2))))
print('\t'.join(['all'] + map(str, np.round(p20_mean[0] / df.shape[0], 2))))
def roc_data(X,Y,clf,n_iter=50,test_size=0.1):
if n_iter is None and test_size is None:
cv = LeaveOneOut()
else:
cv = ShuffleSplit(n_iter=n_iter,test_size=test_size)
n_labels = Y.shape[1]
Y_cv = {i:[] for i in range(n_labels)}
p = {i:[] for i in range(n_labels)}
p_1 = {i:[] for i in range(n_labels)}
p_0 = {i:[] for i in range(n_labels)}
for train, test in cv.split(Y):
clf.fit(X[train,:], Y[train,:])
Y_predicted = clf.predict_proba(X[test,:])
for i in range(Y.shape[1]):
if type(Y_predicted) is list:
p_ = 1 - Y_predicted[i][:,0]
else:
p_ = Y_predicted[:,i]
Y_cv[i] += list(Y[test,i])
p[i] += list(p_)
p_1[i] += list(p_[np.where(Y[test,i]==1)[0]])
p_0[i] += list(p_[np.where(Y[test,i]==0)[0]])
return Y_cv, p, p_1, p_0
def _print_classification_results(classifier, regressors, response, regressors_test, response_test, regressor_names,
messages):
loo = LeaveOneOut()
cv_score = cross_val_score(classifier, regressors, response, cv=loo.split(regressors))
classifier.fit(regressors, response)
messages.AddMessage("Adaboost classifier with " + str(classifier.n_estimators) + " estimators and learning rate "
+ str(classifier.learning_rate))
if regressors_test is None or response_test is None:
regressors_test = regressors
response_test = response
t_set = "Train"
else:
t_set = "Test"
messages.AddMessage("Score (" + t_set + " Set):" + str(classifier.score(regressors_test, response_test)))
messages.AddMessage("Score (Leave one Out):" + str(cv_score.mean()))
messages.AddMessage("Confusion Matrix (" + t_set + " Set):")
confusion = confusion_matrix(response_test, classifier.predict(regressors_test))
labels = ["Non Prospective", "Prospective"]
row_format = "{:6}" + "{:^16}" * (len(labels) + 1)
messages.AddMessage(row_format.format("", "", "Predicted", ""))
messages.AddMessage(row_format.format("True", "", *labels))
for label, row in zip(labels, confusion):
messages.AddMessage(row_format.format("", label, *row))
messages.AddMessage("Area Under the curve (AUC):" + str(roc_auc_score(response_test,
classifier.decision_function(regressors_test))))
messages.AddMessage("Feature importances: ")
importances = [[name, val] for name, val in zip(regressor_names, classifier.feature_importances_)]
for elem in sorted(importances, key=lambda imp: imp[1], reverse=True):
if elem[1] > 0:
messages.AddMessage(elem[0] + ": \t" + str(elem[1]*100) + "%")
return
def redraw(self):
variables = []
if self.includeallcheckBox.isChecked():
for i in range(self.interactionlistWidget.count()):
variables.append(self.interactionlistWidget.item(i).text())
else:
for i in range(self.selectedlistWidget.count()):
variables.append(self.selectedlistWidget.item(i).text())
nX = len(variables)
if nX < 1:
QtWidgets.QMessageBox.critical(self,'Error',"Too few variables selected!",\
QtWidgets.QMessageBox.Ok)
return ()
Yname = self.YcomboBox.currentText()
Lc = DS.Lc[DS.Ic]
Gc = DS.Gc[DS.Ic]
Lcy = Lc[Gc]
Lcx = Lc[-Gc]
data = DS.Raw.loc[DS.Ir, DS.Ic]
Y = data[Lcy]
X = data[Lcx]
if nX > X.shape[0]:
QtWidgets.QMessageBox.critical(self,'Error',"Factors > Observation! \n Reduce factors.",\
QtWidgets.QMessageBox.Ok)
return ()
ny = self.YcomboBox.currentIndex()
Y = Y.values.astype('float')
X = X.values.astype('float')
Y = Y[:, ny]
nr = len(Y)
basey = [Term([LookupFactor(Yname)])]
basex = []
for term in variables:
if term == 'Intercept':
basex = [INTERCEPT]
variables.remove(term)
for term in variables:
vterm = term.split(':')
term_lookup = [LookupFactor(x) for x in vterm]
if len(term_lookup) > 1:
if vterm[0] == vterm[1]:
term_lookup = [EvalFactor(vterm[0] + ' ** 2')]
basex.append(Term(term_lookup))
desc = ModelDesc(basey, basex)
data = np.column_stack((X, Y))
columns = Lcx.tolist()
columns.append(Yname)
data = pd.DataFrame(data, columns=columns)
y, mx = dmatrices(desc, data, return_type='dataframe')
dism = np.linalg.inv(np.dot(mx.T.values, mx.values))
mod = OLS(y, mx)
DOE.res = mod.fit()
# calculation of cross-validation
ypcv = list()
rcv = list()
bres = list()
loo = LeaveOneOut()
loo.get_n_splits(mx)
for train_index, test_index in loo.split(mx):
mx_train = mx.ix[train_index, :]
mx_test = mx.ix[test_index, :]
y_train = y.ix[train_index, :]
y_test = y.ix[test_index, :]
modcv = OLS(y_train, mx_train)
rescv = modcv.fit()
ypcv.append(rescv.predict(mx_test).values[0])
rcv.append(rescv.predict(mx_test).values[0] - y_test.values[0])
bres.append((rescv.params - DOE.res.params).values**2)
bres = pd.DataFrame(bres)
bres = bres.sum() * nr / (nr - 1)
bres = np.sqrt(bres.values)
tres = np.abs(DOE.res.params.values / bres)
pt = 2 * t.pdf(tres, nr)
fig = Figure()
ax = fig.add_subplot(111)
if self.coefradioButton.isChecked():
if DOE.res.params.index[0] == 'Intercept':
ind = np.arange(1, len(DOE.res.params))
vcol = []
for i in ind:
if (DOE.res.pvalues[i] < 0.05): vcol.append('red')
else: vcol.append('blue')
ax.bar(ind, DOE.res.params[1:], align='center', color=vcol)
ax.set_title('Coefficient Value : Intercept {:10.4f}-{:10.4f}-{:10.4f}'.\
format(DOE.res.conf_int().ix[0,0],DOE.res.params[0],DOE.res.conf_int().ix[0,1]))
ax.set_xticklabels(DOE.res.params.index[1:],
rotation='vertical')
cmin = DOE.res.params[1:] - DOE.res.conf_int().ix[1:, 0]
cmax = DOE.res.conf_int().ix[1:, 1] - DOE.res.params[1:]
ax.errorbar(ind,
DOE.res.params[1:],
yerr=[cmin.values, cmax.values],
fmt='o',
ecolor='green')
else:
ind = np.arange(1, len(DOE.res.params) + 1)
ax.bar(ind, DOE.res.params, align='center')
ax.set_title('Coefficient Value : None Intercept')
ax.set_xticklabels(DOE.res.params.index[0:],
rotation='vertical')
cmin = DOE.res.conf_int().ix[0:, 0] - DOE.res.params[0:]
cmax = DOE.res.conf_int().ix[0:, 1] - DOE.res.params[0:]
ax.errorbar(ind,
DOE.res.params[0:],
yerr=[cmin.values, cmax.values],
fmt='o',
ecolor='green')
ax.set_xticks(ind)
ax.set_xlabel('Coefficient Number (except Intercept)')
ax.annotate('red bar: significance 5%',
xy=(0.75, 0.95),
xycoords='figure fraction',
fontsize=8)
elif self.coefpredradioButton.isChecked():
if DOE.res.params.index[0] == 'Intercept':
ind = np.arange(1, len(DOE.res.params))
vcol = []
for i in ind:
if (pt[i] < 0.05): vcol.append('red')
else: vcol.append('blue')
ax.bar(ind, DOE.res.params[1:], align='center', color=vcol)
ax.set_title(
'Coefficient Value : Intercept {:10.4f}-{:10.4f}-{:10.4f}'.
format(DOE.res.params[0] - tres[0] * bres[0] / np.sqrt(nr),
DOE.res.params[0], DOE.res.params[0] +
tres[0] * bres[0] / np.sqrt(nr)))
ax.set_xticklabels(DOE.res.params.index[1:],
rotation='vertical')
ax.errorbar(ind,
DOE.res.params[1:],
yerr=tres[1:] * bres[1:] / np.sqrt(nr),
fmt='o',
ecolor='green')
else:
ind = np.arange(1, len(DOE.res.params) + 1)
ax.bar(ind, DOE.res.params, align='center')
ax.set_title('Coefficient Value : None Intercept')
ax.set_xticklabels(DOE.res.params.index[0:],
rotation='vertical')
ax.errorbar(ind,
DOE.res.params[0:],
yerr=tres[0:] * bres[0:] / np.sqrt(nr),
fmt='o',
ecolor='green')
ax.set_xticks(ind)
ax.set_xlabel('Coefficient Number (except Intercept)')
ax.annotate('red bar: significance 5%',
xy=(0.75, 0.95),
xycoords='figure fraction',
fontsize=8)
elif self.fitradioButton.isChecked():
yf = DOE.res.fittedvalues.tolist()
resid = DOE.res.resid.tolist()
ax.scatter(y, yf, color='red', alpha=0.3, marker='o')
ax.set_ylabel('Fitted Values', color='red')
ax.tick_params('y', colors='red')
ax1 = ax.twinx()
ax1.scatter(y, resid, color='blue', alpha=0.3, marker='o')
ax1.set_ylabel('Residuals', color='blue')
ax1.tick_params('y', colors='blue')
xmin, xmax = ax.get_xlim()
ax.set_ylim([xmin, xmax])
df = DOE.res.df_resid
vares = np.sum(DOE.res.resid**2) / df
rmsef = np.sqrt(vares)
vary = np.var(y.values)
evar = (1 - vares / vary) * 100
ax.set_title(
'df {:3.0f}; RMSEF {:6.2f}; Exp.Var.{:5.1f}%'.format(
df, rmsef, evar))
ax.add_line(Line2D([xmin, xmax], [xmin, xmax], color='red'))
ax1.add_line(Line2D([xmin, xmax], [0, 0], color='blue'))
ax.set_xlabel('Measured Values')
if self.VcheckBox.isChecked():
Lr = DOE.res.model.data.row_labels
for i, txt in enumerate(Lr):
ax.annotate(str(txt), (y.ix[i], yf[i]))
elif self.predradioButton.isChecked():
ax.scatter(y, ypcv, color='red', alpha=0.3, marker='o')
ax.set_ylabel('CV Predicted Values', color='red')
ax.tick_params('y', colors='red')
ax1 = ax.twinx()
ax1.scatter(y, rcv, color='blue', alpha=0.3, marker='o')
ax1.set_ylabel('CV Residuals', color='blue')
ax1.tick_params('y', colors='blue')
xmin, xmax = ax.get_xlim()
ax.set_ylim([xmin, xmax])
ax.set_xlabel('Measured Values')
df = DS.Raw.shape[0]
varcv = np.sum(np.array(rcv)**2) / df
rmsecv = np.sqrt(varcv)
vary = np.var(y.values)
evar = (1 - varcv / vary) * 100
ax.set_title(
'df {:3.0f}; RMSECV {:6.2f}; Exp.Var.{:5.1f}%'.format(
df, rmsecv, evar))
ax.add_line(Line2D([xmin, xmax], [xmin, xmax], color='red'))
ax1.add_line(Line2D([xmin, xmax], [0, 0], color='blue'))
if self.VcheckBox.isChecked():
Lr = DOE.res.model.data.row_labels
for i, txt in enumerate(Lr):
ax.annotate(str(txt), (y.ix[i], ypcv[i]))
elif self.levradioButton.isChecked():
Ftable = surtabDlg.launch(None)
if len(np.shape(Ftable)) == 0: return ()
if np.argmax(Ftable['X axis'].values) == np.argmax(
Ftable['Y axis'].values):
QtWidgets.QMessageBox.critical(self,'Error',"Two variables on the same axis",\
QtWidgets.QMessageBox.Ok)
return ()
fig = plt.figure()
ax = fig.add_subplot(111)
npts = 20
xname = Ftable[(Ftable['X axis'] == True).values].index[0]
yname = Ftable[(Ftable['Y axis'] == True).values].index[0]
cname = Ftable[(Ftable['Constant'] == True).values].index.tolist()
cvalue = Ftable.loc[(Ftable['Constant'] == True).values, 'value']
zname = Yname
x = np.linspace(float(Ftable['min'][xname]),
float(Ftable['max'][xname]), npts)
y = np.linspace(float(Ftable['min'][yname]),
float(Ftable['max'][yname]), npts)
px = []
py = []
for i in range(npts):
for j in range(npts):
px.append(x[i])
py.append(y[j])
data = pd.DataFrame({xname: px, yname: py, zname: px})
xtitle = ''
for i in range(len(cname)):
xtitle = xtitle + cname[i] + ' = ' + str(
cvalue.values.tolist()[i])
data[cname[i]] = np.ones(npts**2) * float(cvalue[i])
my, mx = dmatrices(desc, data, return_type='dataframe')
pz = np.diag(np.dot(np.dot(mx, dism), mx.T))
px = np.array(px)
py = np.array(py)
pz = np.array(pz)
z = plt.mlab.griddata(px, py, pz, x, y, interp='linear')
plt.contour(x, y, z, 15, linewidths=0.5, colors='k')
plt.contourf(x, y, z, 15, cmap=plt.cm.rainbow)
plt.colorbar()
ax.set_xlabel(xname)
ax.set_ylabel(yname)
ax.set_title(xtitle)
ax.set_xlim([px.min(), px.max()])
ax.set_ylim([py.min(), py.max()])
elif self.surradioButton.isChecked():
Ftable = surtabDlg.launch(None)
if len(np.shape(Ftable)) == 0: return ()
if np.argmax(Ftable['X axis'].values) == np.argmax(
Ftable['Y axis'].values):
QtWidgets.QMessageBox.critical(self,'Error',"Two variables on the same axis",\
QtWidgets.QMessageBox.Ok)
return ()
fig = plt.figure()
ax = fig.add_subplot(111)
npts = 100
xname = Ftable[(Ftable['X axis'] == True).values].index[0]
yname = Ftable[(Ftable['Y axis'] == True).values].index[0]
cname = Ftable[(Ftable['Constant'] == True).values].index.tolist()
cvalue = Ftable.loc[(Ftable['Constant'] == True).values, 'value']
zname = Yname
x = np.linspace(float(Ftable['min'][xname]),
float(Ftable['max'][xname]), npts)
y = np.linspace(float(Ftable['min'][yname]),
float(Ftable['max'][yname]), npts)
px = []
py = []
for i in range(npts):
for j in range(npts):
px.append(x[i])
py.append(y[j])
data = pd.DataFrame({xname: px, yname: py, zname: px})
xtitle = ''
for i in range(len(cname)):
xtitle = xtitle + cname[i] + ' = ' + str(
cvalue.values.tolist()[i])
data[cname[i]] = np.ones(npts**2) * float(cvalue[i])
my, mx = dmatrices(desc, data, return_type='dataframe')
pz = DOE.res.predict(mx)
px = np.array(px)
py = np.array(py)
pz = np.array(pz)
z = plt.mlab.griddata(px, py, pz, x, y, interp='linear')
plt.contour(x, y, z, 15, linewidths=0.5, colors='k')
plt.contourf(x, y, z, 15, cmap=plt.cm.rainbow)
plt.colorbar()
ax.set_xlabel(xname)
ax.set_ylabel(yname)
ax.set_title(xtitle)
ax.set_xlim([px.min(), px.max()])
ax.set_ylim([py.min(), py.max()])
elif self.dismradioButton.isChecked():
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(dism)
fig.colorbar(cax)
ax.set_title('Trace = {:10.4f}'.format(np.trace(dism)))
elif self.inflradioButton.isChecked():
mxc = preprocessing.scale(mx.values,
with_mean=True,
with_std=False)
mxc2 = mxc**2
infl = np.sum(mxc2, axis=0) * np.diag(dism)
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(infl.reshape(1, -1), cmap='gray_r')
fig.colorbar(cax)
ax.yaxis.grid(False)
ax.tick_params(axis='y',
which='both',
left='off',
right='off',
labelleft='off')
ax.set_xlabel('Inlaction Factor')
if self.XcheckBox.isChecked():
if self.XlineEdit.text():
ax.set_xlabel(self.XlineEdit.text())
else:
ax.set_xlabel('')
if self.YcheckBox.isChecked():
if self.YlineEdit.text():
ax.set_ylabel(self.YlineEdit.text())
else:
ax.set_ylabel('')
if self.XGcheckBox.isChecked():
ax.xaxis.grid(True)
else:
ax.xaxis.grid(False)
if self.YGcheckBox.isChecked():
ax.yaxis.grid(True)
else:
ax.yaxis.grid(False)
if not self.XMcheckBox.isChecked():
ax.tick_params(axis='x',
which='both',
bottom='off',
top='off',
labelbottom='off')
if not self.YMcheckBox.isChecked():
ax.tick_params(axis='y',
which='both',
left='off',
right='off',
labelleft='off')
self.rmmpl()
self.addmpl(fig)
import numpy as np
from sklearn.model_selection import LeaveOneOut
# ----------------------------------------------------
'''
class sklearn.model_selection.LeaveOneOut()
'''
# ----------------------------------------------------
X = np.array([1, 2, 3, 4])
y = np.array([5, 6, 7, 8])
loo = LeaveOneOut()
print(loo.get_n_splits(X))
print(loo)
loo = LeaveOneOut()
for train_index, test_index in loo.split(X):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
print('X_train \n', X_train)
print('X_test \n', X_test)
print('y_train \n', y_train)
print('y_test \n', y_test)
print('*********************')
def train(self):
with open(os.path.join(self.results_folder, "log.txt"), "w") as f_log:
for train, test in LeaveOneOut().split(self.dfs):
train_set = [self.dfs[i] for i in train]
test_set = self.dfs[test[0]]
# Create sentence and label lists
sentences_list = []
labels_list = []
for i, book in enumerate(train_set):
sentences_list.extend(book.sentence.values)
labels_list.extend(book.label.values)
f_log.write("Length book: " + str(len(sentences_list[i])) +
'\n')
f_log.write("Sentences: " + str(len(sentences_list)) +
", labels:" + str(len(labels_list)) + '\n')
MAX_LEN = 128
# We need to add special tokens at the beginning and end of each sentence for BERT to work properly
sentences_train = [
self.tokenizer.encode_plus(sent,
add_special_tokens=True,
max_length=MAX_LEN)
for i, sent in enumerate(sentences_list)
]
le = LabelEncoder()
labels_train = labels_list
f_log.write(str(labels_train[:10]) + '\n')
f_log.write('Analyze labels' + '\n')
le.fit(labels_train)
le_name_mapping = dict(
zip(le.classes_, le.transform(le.classes_)))
f_log.write(str(le_name_mapping) + '\n')
labels_train = le.fit_transform(labels_train)
# Use the BERT tokenizer to convert the tokens to their index numbers in the BERT vocabulary
input_ids_train = [
inputs["input_ids"] for inputs in sentences_train
]
# Pad our input tokens
input_ids_train = pad_sequences(input_ids_train,
maxlen=MAX_LEN,
truncating="post",
padding="post")
# Create attention masks
attention_masks_train = []
# Create a mask of 1s for each token followed by 0s for padding
for seq in input_ids_train:
seq_mask_train = [float(i > 0) for i in seq]
attention_masks_train.append(seq_mask_train)
# Use train_test_split to split our data into train and validation sets for training
train_inputs, train_labels = input_ids_train, labels_train
train_masks, _ = attention_masks_train, input_ids_train
# Convert all of our data into torch tensors, the required datatype for our model
train_inputs = torch.tensor(train_inputs).to(torch.int64)
train_labels = torch.tensor(train_labels).to(torch.int64)
train_masks = torch.tensor(train_masks).to(torch.int64)
batch_size = 32
# Create an iterator of our data with torch DataLoader. This helps save on memory during training
# because, unlike a for loop, with an iterator the entire dataset does not need to be loaded into
# memory
train_data = TensorDataset(train_inputs, train_masks,
train_labels)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=batch_size)
torch.cuda.empty_cache()
# BINARY CLASSIFIER
model = BertForSequenceClassification.from_pretrained(
"bert-base-uncased", num_labels=2)
model.cuda()
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.01
}, {
'params': [
p for n, p in param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.0
}]
# This variable contains all of the hyperparemeter information our training loop needs
optimizer = BertAdam(optimizer_grouped_parameters,
lr=2e-5,
warmup=.1)
train_loss_set = []
# Number of training epochs (authors recommend between 2 and 4)
epochs = 10
device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
torch.cuda.get_device_name(0)
for _ in trange(epochs, desc="Epoch"):
# Training
# Set our model to training mode (as opposed to evaluation mode)
model.train()
# Tracking variables
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
# Train the data for one epoch
for step, batch in enumerate(train_dataloader):
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack the inputs from our dataloader
b_input_ids, b_input_mask, b_labels = batch
# Clear out the gradients (by default they accumulate)
optimizer.zero_grad()
# Forward pass
loss = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
train_loss_set.append(loss.item())
# Backward pass
loss.backward()
# Update parameters and take a step using the computed gradient
optimizer.step()
# Update tracking variables
tr_loss += loss.item()
nb_tr_examples += b_input_ids.size(0)
nb_tr_steps += 1
f_log.write("Train loss: {}".format(tr_loss /
nb_tr_steps) + '\n')
plt.figure(figsize=(15, 8))
plt.title("Training loss")
plt.xlabel("Batch")
plt.ylabel("Loss")
plt.plot(train_loss_set)
plt.savefig(self.img_folder + 'train' + str(test[0]) + '.png')
model_to_save = model
WEIGHTS_NAME = "BERT_Novel_test" + str(test[0]) + ".bin"
OUTPUT_DIR = self.models_folder
output_model_file = os.path.join(OUTPUT_DIR, WEIGHTS_NAME)
f_log.write(str(output_model_file) + '\n')
torch.save(model_to_save.state_dict(), output_model_file)
state_dict = torch.load(output_model_file)
model.load_state_dict(state_dict)
sentences6 = test_set.sentence.values
f_log.write(str(len(sentences6)) + '\n')
labels6 = test_set.label.values
labels_test = labels6
sentences11 = sentences6
sentences_test = [
self.tokenizer.encode_plus(sent,
add_special_tokens=True,
max_length=MAX_LEN)
for i, sent in enumerate(sentences11)
]
f_log.write('Analyze labels test' + '\n')
le.fit(labels_test)
le_name_mapping = dict(
zip(le.classes_, le.transform(le.classes_)))
f_log.write(str(le_name_mapping) + '\n')
labels_test = le.fit_transform(labels_test)
MAX_LEN = 128
# Use the BERT tokenizer to convert the tokens to their index numbers in the BERT vocabulary
input_ids1 = [inputs["input_ids"] for inputs in sentences_test]
# Pad our input tokens
input_ids1 = pad_sequences(input_ids1,
maxlen=MAX_LEN,
truncating="post",
padding="post")
# Create attention masks
attention_masks1 = []
# Create a mask of 1s for each token followed by 0s for padding
for seq in input_ids1:
seq_mask1 = [float(i > 0) for i in seq]
attention_masks1.append(seq_mask1)
f_log.write(str(len(attention_masks1[0])) + '\n')
prediction_inputs = torch.tensor(input_ids1).to(torch.int64)
prediction_masks = torch.tensor(attention_masks1).to(
torch.int64)
prediction_labels = torch.tensor(labels_test).to(torch.int64)
batch_size = 32
prediction_data = TensorDataset(prediction_inputs,
prediction_masks,
prediction_labels)
prediction_sampler = SequentialSampler(prediction_data)
prediction_dataloader = DataLoader(prediction_data,
sampler=prediction_sampler,
batch_size=batch_size)
# Prediction on test set
# Put model in evaluation mode
model.eval()
# Tracking variables
predictions, true_labels = [], []
# Predict
for batch in prediction_dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack the inputs from our dataloader
b_input_ids, b_input_mask, b_labels = batch
# Telling the model not to compute or store gradients, saving memory and speeding up prediction
with torch.no_grad():
# Forward pass, calculate logit predictions
logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Store predictions and true labels
predictions.append(logits)
true_labels.append(label_ids)
f_log.write(
str(len(predictions)) + ' ' + str(len(true_labels)) + '\n')
f_log.write(str(predictions[0][0]) + '\n')
# Import and evaluate each test batch using Matthew's correlation coefficient
matthews_set = []
for i in range(len(true_labels)):
matthews = matthews_corrcoef(
true_labels[i],
np.argmax(predictions[i], axis=1).flatten())
matthews_set.append(matthews)
# Flatten the predictions and true values for aggregate Matthew's evaluation on the whole dataset
flat_predictions = [
item for sublist in predictions for item in sublist
]
flat_predictions = np.argmax(flat_predictions,
axis=1).flatten()
flat_true_labels = [
item for sublist in true_labels for item in sublist
]
f_log.write(
str(len(flat_predictions) + ' ' + len(flat_true_labels)) +
'\n')
f_log.write(
str(flat_predictions[989:994] + ' ' +
flat_true_labels[989:994]) + '\n')
f_log.write(
str(flat_predictions[0:11] + ' ' +
flat_true_labels[0:11]) + '\n')
f_log.write('Classification Report' + '\n')
f_log.write(
str(
classification_report(flat_true_labels,
flat_predictions)) + '\n')
f_log.write(
str(confusion_matrix(flat_true_labels, flat_predictions)) +
'\n')
def testXGBoostPredictions(data, parameters, x_cols, y_cols, plots=False):
"""
Testing XGBoost prediction accuracies.
Arguments:
data {array} -- Labeled data for classifier testing.
x_cols {array} -- x columns
y_cols {array} -- y columns
parameters {namedtuple} -- Parameters for the tree classifier. Using named tuple to keep things tidy.
Keyword Arguments:
plots {bool} -- Used for plotting (default: {False})
"""
x = data.loc[:, x_cols]
y = data.loc[:, y_cols]
loo = LeaveOneOut()
loo.get_n_splits(data)
n = loo.split(data)
xgbClassifier = xgb.XGBClassifier()
accuracy_a = []
real_label = []
pred_label = []
for train_index, test_index in n: #Each row is test data once
xtrain, xtest = x.iloc[train_index], x.iloc[test_index]
ytrain, ytest = y.iloc[train_index], y.iloc[test_index]
#Fitting train data
xgbClassifier = xgbClassifier.fit(xtrain, ytrain.values.ravel())
#Predictions
ypred = xgbClassifier.predict(xtest)
pred_label.append(ypred)
real_label.append(ytest.values)
#Accuracy
acc = accuracy_score(ytest, ypred)
accuracy_a.append(acc)
pred_label_df = pd.DataFrame(columns=["label"])
real_label_df = pd.DataFrame(columns=["label"])
#Forming the dataframes
for row in range(0, len(pred_label)):
label_str = pred_label[row][0]
pred_label_df.loc[row] = label_str
for row in range(0, len(real_label)):
label_str = real_label[row][0][0]
real_label_df.loc[row] = label_str
if (plots): #Plotting tree and accuracy heatmap
cm = confusion_matrix(real_label_df, pred_label_df)
cm_df = pd.DataFrame(cm, ["Fall", "Normal"], ["Fall", "Normal"])
sn.set(font_scale=1.5)
sn.heatmap(cm_df, annot=True, annot_kws={"size": 32}, fmt='d')
plt.savefig("../figs/xgboost_heatmap.png",
facecolor="w",
bbox_inches="tight")
plt.show()
avg_acc = np.mean(accuracy_a)
#Checking accuracy
print("Tree average accuracy: ", round(avg_acc, 2)) #2 decimals
#More detailed report
print(classification_report(real_label_df, pred_label_df))
return (avg_acc, real_label_df, pred_label_df)
def main():
parser = argparse.ArgumentParser(
description='Run a particular experiment using an ElasticNet estimator'
)
parser.add_argument('--data_dir',
dest="data_dir",
type=str,
required=True,
help='Directory where data are located')
parser.add_argument('--output_dir',
dest="output_dir",
type=str,
required=True,
help='Directory where we are going to save the resuts')
parser.add_argument('--con_type',
dest="con_type",
type=str,
required=True,
choices=['look_neg_look_neut', 'reg_neg_look_neg'],
help='Which contrast maps to take as input')
parser.add_argument('--target_var',
dest="target_var",
type=str,
required=True,
help='Which variable to take as target')
parser.add_argument('--n_alphas',
dest="n_alphas",
type=int,
default=1000,
help='Number of alphas to try for optimization')
parser.add_argument('--transform',
dest="transform",
type=str,
choices=['yeo-johnson', 'box-cox'],
help='Transform target variable')
opts = parser.parse_args()
if opts.transform:
msg = "Experiment to predict %s transformed %s from %s contrast maps with %d alphas" % (
opts.transform, opts.target_var, opts.con_type, opts.n_alphas)
print(msg)
else:
msg = "Experiment to predict untransformed %s from %s contrast maps with %d alphas" % (
opts.target_var, opts.con_type, opts.n_alphas)
print(msg)
data_dir = os.path.abspath(opts.data_dir)
if Path(data_dir).exists() is False:
raise print("input directory does not exist")
# Load data
print("Loading data...")
X, y = load_data(data_dir, opts.con_type, opts.target_var)
# Build classifier
cv_outer = LeaveOneOut()
print("Running experiment...")
if opts.transform:
y_pred, y_true, list_models = run_transform(X,
y,
opts.transform,
cv_outer=cv_outer,
n_alphas=opts.n_alphas)
else:
y_pred, y_true, list_models = run(X,
y,
cv_outer=cv_outer,
n_alphas=opts.n_alphas)
from sklearn.metrics import r2_score, mean_squared_error
r = np.corrcoef(y_true, y_pred)[0, 1]
r2 = r2_score(y_true, y_pred)
mse = mean_squared_error(y_true, y_pred)
print("experiment gives r =%.3f, R2 = %.3f, MSE = %.3f" % (r, r2, mse))
print("Saving results...")
# Create output_directory for the given case (target->Input)
if opts.transform:
output_dir = opj(opts.output_dir,
opts.transform + "_" + opts.target_var, opts.con_type)
else:
output_dir = opj(opts.output_dir, opts.target_var, opts.con_type)
output_dir = os.path.abspath(output_dir)
Path(output_dir).mkdir(parents=True, exist_ok=True)
save_data(output_dir, y_pred, y_true, list_models)
def compute_acc_conf(x,
y,
confounds,
verbose=False,
balanced=True,
loo=False,
nfolds=10,
gs_kfolds=5,
optimize=True,
C=.01):
encoder = preprocessing.LabelEncoder()
encoder.fit(y)
if loo:
cv = LeaveOneOut(len(y))
else:
cv = StratifiedKFold(y=encoder.transform(y), n_folds=nfolds)
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
all_tpr = []
total_test_score = []
y_pred = []
# clf_array = []
bc_all = []
prec = []
recall = []
if len(np.unique(y)) == 1:
print('Unique class: 100%', np.sum(encoder.transform(y) == 0) / len(y))
return (1., 0., len(y))
for i, (train, test) in enumerate(cv):
select_x = x.copy()
# betacluster = bc.BetaCluster(crm.transform(confounds[train,:],select_x[train,:]),encoder.transform(y[train]),100,k_feature=200)
# bc_all.append(betacluster)
if balanced:
clf = SVC(kernel='linear', class_weight='balanced', C=C)
else:
clf = SVC(kernel='linear', C=C)
if len(confounds) == 0:
xtrain = select_x[train, :]
xtest = select_x[test, :]
else:
crm = ConfoundsRm(confounds[train, :], select_x[train, :])
xtrain = crm.transform(confounds[train, :], select_x[train, :])
xtest = crm.transform(confounds[test, :], select_x[test, :])
ytrain = encoder.transform(y[train])
ytest = encoder.transform(y[test])
# clf.probability = True
if optimize:
clf, score = plib.grid_search(clf,
xtrain,
ytrain,
n_folds=gs_kfolds,
verbose=verbose)
clf.fit(xtrain, ytrain)
total_test_score.append(clf.score(xtest, ytest))
# clf_array.append(clf)
prec.append(metrics.precision_score(ytest, clf.predict(xtest)))
recall.append(metrics.recall_score(ytest, clf.predict(xtest)))
if loo:
y_pred.append(clf.predict(xtest))
if verbose:
print('nSupport: ', clf.n_support_)
print("Train:", clf.score(xtrain, ytrain))
print("Test :", clf.score(xtest, ytest))
print("Prediction :", clf.predict(xtest))
print("Real Labels:", ytest)
print('Precision:', prec[-1], 'Recall:', recall[-1])
y_pred = np.array(y_pred)[:, 0]
if loo:
total_std_test_score = estimate_std(
metrics.accuracy_score(encoder.transform(y), np.array(y_pred)),
len(y))
print('Mean:', np.mean(total_test_score), 'Std:', total_std_test_score,
'AvgPrecision:', np.mean(prec), 'AvgRecall:', np.mean(recall))
return [
np.mean(total_test_score), total_std_test_score,
len(y), y_pred
]
else:
print('Mean:', np.mean(total_test_score), 'Std:',
np.std(total_test_score), 'AvgPrecision:', np.mean(prec),
'AvgRecall:', np.mean(recall))
return [np.mean(total_test_score), np.std(total_test_score), len(y)]
cls(n_splits=3, random_state=0))
assert tokenize(cls(n_splits=3, random_state=0)) != tokenize(
cls(n_splits=3, random_state=2))
assert tokenize(cls(n_splits=3, random_state=0)) != tokenize(
cls(n_splits=4, random_state=0))
cv = cls(n_splits=3)
assert compute_n_splits(cv, np_X, np_y, np_groups) == 3
with assert_dask_compute(False):
assert compute_n_splits(cv, da_X, da_y, da_groups) == 3
@pytest.mark.parametrize("cvs", [(LeaveOneOut(), ),
(LeavePOut(2), LeavePOut(3))])
def test_leave_out(cvs):
tokens = []
for cv in cvs:
assert tokenize(cv) == tokenize(cv)
tokens.append(cv)
assert len(set(tokens)) == len(tokens)
cv = cvs[0]
sol = cv.get_n_splits(np_X, np_y, np_groups)
assert compute_n_splits(cv, np_X, np_y, np_groups) == sol
with assert_dask_compute(True):
assert compute_n_splits(cv, da_X, da_y, da_groups) == sol
# 'min_samples_split': [2, 3, 4, 5, 10, 15],
# 'max_depth': [3, 4, 5, 10],
# "criterion": ["gini"]
# }
# clf = GridSearchCV(
# RandomForestClassifier(),
# parameters,
# cv=3,
# n_jobs=-1
# )
# clf.fit(all_data, all_label)
# print(clf.best_estimator_)
# L法
print("--- L法 ---")
loo = LeaveOneOut()
count = [[0, 0], [0, 0]]
for train_index, test_index in loo.split(all_data):
train_data = [all_data[i] for i in train_index]
train_label = [all_label[i] for i in train_index]
test_data = [all_data[i] for i in test_index]
test_label = [all_label[i] for i in test_index]
clf = RandomForestClassifier(bootstrap=True,
ccp_alpha=0.0,
class_weight=None,
criterion='gini',
max_depth=10,
max_features=6,
max_leaf_nodes=None,
max_samples=None,
min_impurity_decrease=0.0,
def _print_train_results(classifier_name, classifier, regressors, response,
regressor_names, leave_one_out):
"""
_print_train_results
Performs validation tests of the model and prints the results
:param classifier_name: Name of the classifier method
:param classifier: Classifier object
:param regressors: numpy array with the regressors used to train the model
:param response: numpy array with the response used to train the model
:param regressor_names: List with the name of the regressors
:param leave_one_out: Boolean, true to perform leave-one-out cross-validation, otherwise perform default cross
validation
:return: None
"""
global MESSAGES
_verbose_print("classifier_name: {}".format(classifier_name))
_verbose_print("classifier: {}".format(classifier))
_verbose_print("regressor_names: {}".format(regressor_names))
_verbose_print("leave_one_out: {}".format(leave_one_out))
MESSAGES.AddMessage("{} classifier with parameters: \n {}".format(
classifier_name,
str(classifier.get_params()).replace("'", "")))
if leave_one_out:
# create a leave-one-out instance to execute the cross-validation
loo = LeaveOneOut()
start = timer()
cv_score = cross_val_score(classifier,
regressors,
response,
cv=loo.split(regressors))
end = timer()
n_tests = len(response)
MESSAGES.AddMessage("Score (Leave one Out):" + str(cv_score.mean()))
else:
start = timer()
cv_score = cross_val_score(classifier, regressors, response)
end = timer()
n_tests = 3
MESSAGES.AddMessage("Score (3-Fold):" + str(cv_score.mean()))
# Print validation time
MESSAGES.AddMessage(
"Testing time: {:.3f} seconds, {:.3f} seconds per test".format(
end - start, (end - start) / n_tests))
# Print confusion matrix
MESSAGES.AddMessage("Confusion Matrix (Train Set):")
confusion = confusion_matrix(response, classifier.predict(regressors))
labels = ["Non Deposit", "Deposit"]
row_format = "{:6}" + "{:^16}" * (len(labels) + 1)
MESSAGES.AddMessage(row_format.format("", "", "Predicted", ""))
MESSAGES.AddMessage(row_format.format("True", "", *labels))
for label, row in zip(labels, confusion):
MESSAGES.AddMessage(row_format.format("", label, *row))
# Some classifiers do not have decision_function attribute but count with predict_proba instead
# TODO: Generalize to anything that does not have decision_function "Easier to ask for forgiveness than permission"
if classifier_name in ["Random Forest"]:
des_fun = classifier.predict_proba(
regressors)[:, classifier.classes_ == 1]
else:
des_fun = classifier.decision_function(regressors)
MESSAGES.AddMessage("Area Under the curve (AUC): {}".format(
roc_auc_score(response, des_fun)))
# Give the importance of the features if it is supported
# TODO: Generalize to anything that does have feature_importances_ "Easier to ask for forgiveness than permission"
if classifier_name == "Adaboost":
MESSAGES.AddMessage("Feature importances: ")
importances = [[name, val * 100] for name, val in zip(
regressor_names, classifier.feature_importances_)]
long_word = max([len(x) for x in regressor_names])
row_format = "{" + ":" + str(long_word) + "} {:4.1f}%"
# Print regressors in descending importance, omit the ones with 0 importance
for elem in sorted(importances, key=lambda imp: imp[1], reverse=True):
if elem[1] > 0:
MESSAGES.AddMessage(row_format.format(*elem))
return
stop_ind = start_ind + test_size
train_targets = followup_total_PANSS[train_index]
test_targets = followup_total_PANSS[test_index]
test_subjects = test_subjects + list(np.array(subjectids)[test_index])
# do supervised site correction?
if site_correction == 'comBat_supervised':
# generate (outer) train and test data and metadata
train_data = logm_connectivity_data[train_index, :]
test_data = logm_connectivity_data[test_index, :]
train_metadata = metadata.iloc[train_index, :]
# generate loo training prediction
loo = LeaveOneOut()
inner_predictions = np.zeros((train_size, ))
inner_train_indices, inner_test_indices = min_two_test_CV(
site[train_index])
#for j in range(len(inner_train_indices)) :
#for inner_train_index, inner_test_index in loo.split(train_data) :
skf = StratifiedKFold(n_splits=3)
for inner_train_index, inner_test_index in skf.split(
train_data, site[train_index]):
# print ('i = ' +str(i))
# print ('j = ' +str(j))
#
# inner_train_index = inner_train_indices[j]
# inner_test_index = inner_test_indices[j]
#
def main(argv):
filename = argv[0]
t = float(argv[1]) # threshold for logistic regression (default=0.5)
dup = int(argv[2]) # if 1, bad queries will be duplicated
subset = 'cache' # column title for precision of cache
full = 'full' # column title for precision of full db
df = pd.read_csv('../../data/cache_selection_structured/' + filename)
df = df.drop(['query', 'freq'], axis=1)
df = df.fillna(0)
df['label'] = np.where(df['full'] > df['cache'], 1, 0)
if dup:
print('duping..')
bads = df[df['label'] == 1]
df = df.append(bads, ignore_index=True)
X = df.drop(['label'], axis=1)
y = df['label']
p20_mean = np.zeros([1, 6])
bad_mean = np.zeros([1, 6])
ml_average_rare = 0
ql_average_rare = 0
best_average_rare = 0
loo = LeaveOneOut()
bad_counter = 0
for train_index, test_index in loo.split(X):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]
X_train = X_train.drop([subset, full], axis=1)
p12 = X_test[subset].iloc[0]
p100 = X_test[full].iloc[0]
is_bad = p12 < p100
X_test = X_test.drop([subset, full], axis=1)
# compute query likelihood based effectiveness
ql_cache = np.mean(X_test['ql_0_0'] + X_test['ql_0_1'] +
X_test['ql_1_0'] + X_test['ql_2_0'])
ql_rest = np.mean(X_test['ql_rest_0_0'] + X_test['ql_rest_0_1'] +
X_test['ql_rest_1_0'] + X_test['ql_rest_2_0'])
#ql_pred = X_test['ql_0_1'].iloc[0] < X_test['ql_rest_0_1'].iloc[0]
ql_pred = 1 if ql_cache < ql_rest else 0
ql = p12 if ql_pred == 0 else p100
# learn the model
sc = MinMaxScaler().fit(X_train)
X_train = sc.transform(X_train)
X_test = sc.transform(X_test)
# print("\ttraining balanced LR..")
lr = linear_model.LogisticRegression(class_weight='balanced')
lr.fit(X_train, y_train)
# print("\ttraining mean accuracy = %.2f" % lr.score(X_train, y_train))
# print("\ttesting mean accuracy = %.2f" % lr.score(X_test, y_test))
y_prob = lr.predict_proba(X_test)
y_pred = y_prob[:, 1] > t
y_pred = y_pred.astype('uint8')
# print('\t t = %.2f results:' % t)
# print_results(y_test, y_pred)
# compute ML based effectiveness
ml = p12 if y_pred[0] == 0 else p100
best = p12 if y_test.iloc[0] == 0 else p100
rnd = p12 if np.random.randint(0, 2) == 1 else p100
p20_mean += [p12, p100, ml, ql, best, rnd]
if is_bad:
#bad_mean += [p12[0], p100[0], ml[0], ql[0], best[0], rnd[0]]
bad_mean += [p12, p100, ml, ql, best, rnd]
bad_counter += 1
print('final results:')
print('\t'.join(
map(str, ['set', 'cache', 'db', 'ml', 'ql', 'best', 'rand'])))
print('\t'.join(['bad'] +
map(str, np.round(bad_mean[0] / bad_counter, 2))))
print('\t'.join(['all'] +
map(str, np.round(p20_mean[0] / df.shape[0], 2))))
def HADA(sourceGraph, targetGraph, labels, settings):
# initialisation
subject = 150
overallResult_PCC = np.zeros((subject, 32))
overallResult_TSW = np.zeros((subject, 32))
allSV = np.empty((0, sourceGraph.shape[1]), int)
allTV = np.empty((0, targetGraph.shape[1]), int)
allpredTV = np.empty((0, targetGraph.shape[1]), int)
testlabel = []
# Create training and testing sets
loo = LeaveOneOut()
loo.get_n_splits(sourceGraph)
for train_index, test_index in loo.split(sourceGraph):
rearrangedPredictorView = np.concatenate((np.transpose(
sourceGraph[train_index]), np.transpose(sourceGraph[test_index])),
axis=1)
rearrangedTargetView = np.concatenate((np.transpose(
targetGraph[train_index]), np.transpose(targetGraph[test_index])),
axis=1)
## Domain Alignment (DA) using ARGA and Similarity matrix learning using SIMLR
simlr = SIMLR.SIMLR_LARGE(1, 50, 0)
enc = Encoder(settings)
## STEP 1: Hierarchical Domain Alignment for traing samples
print("Hierarchical Domain Alignment for traing samples")
print("level 1")
Simlarity2, _, _, _ = simlr.fit(targetGraph[train_index])
encode_S_T = enc.erun(Simlarity2, sourceGraph[train_index])
# H denotes the number of hierarchical levels
H = 2
temporary = encode_S_T
for number in range(1, H):
print("level ", H)
encode_train__TV_A = enc.erun(Simlarity2, temporary)
temporary = encode_train__TV_A
## STEP 2: Target Graph Prediction
## STEP 2.1: Source graph embedding of training and testing subjects
test__train__SV = np.vstack(
(sourceGraph[train_index], sourceGraph[test_index]))
print("Source graph embedding of training and testing subjects...")
Simlarity1, _, _, _ = simlr.fit(test__train__SV)
encode_test__train__SV = enc.erun(Simlarity1, test__train__SV)
## STEP 2.2: Connectomic Manifold Learning using SIMLR
print("SIMLR...")
SALL, FALL, val, ind = simlr.fit(encode_test__train__SV)
SY, FY, val, ind = simlr.fit(encode_train__TV_A)
# number of neighbors for trust score
TS_bestNb = 5
# get the best neighbors in the learned manifold of the regularized source graph embeddings
sall = SALL.todense()
Index_ALL = np.argsort(-sall, axis=0)
des = np.sort(-sall, axis=0)
Bvalue_ALL = -des
# get the best neighbors in the learned manifold of the hierarchically aligned source and target graph embeddings
sy = SY.todense()
Index_Y = np.argsort(-sy, axis=0)
desy = np.sort(-sy, axis=0)
Bvalue_Y = -desy
# make prediction for each testing subject
for testingSubject in range(1, 2):
print "testing subject:", test_index
# get this testing subject's rearranged index and original index
tSubjectIndex = (sourceGraph[train_index].shape[0] -
2) + testingSubject
tSubjectOriginalIndex = test_index
# compute Tscore for each neighbor
trustScore = np.ones((TS_bestNb, TS_bestNb))
newWeight_TSW = np.ones(TS_bestNb)
for neighbor in range(0, TS_bestNb):
neighborIndex = Index_ALL[tSubjectIndex, neighbor]
temp_counter = 0
while (neighborIndex > sourceGraph[train_index].shape[0]):
# best neighbor is a testing data
temp_counter = temp_counter + 1
neighborIndex = Index_ALL[tSubjectIndex,
(TS_bestNb + temp_counter)]
if (temp_counter != 0):
neighborSequence = TS_bestNb + temp_counter
else:
neighborSequence = neighbor
#print(neighborIndex)
# get top nb neighbors in mappedX
neighborListX = Index_ALL[neighborIndex, 0:TS_bestNb]
# get top nb neighbors in mappedY
neighborListY = Index_Y[neighborIndex, 0:TS_bestNb]
# calculate trust score
trustScore[TS_bestNb - 1, neighbor] = len(
np.intersect1d(np.array(neighborListX),
np.array(neighborListY)))
# calculate new weight (TS * Similarity)
newWeight_TSW[neighbor] = exp(
trustScore[TS_bestNb - 1, neighbor] / TS_bestNb *
Bvalue_ALL[tSubjectIndex, neighborSequence])
#reconstruct with Tscore and similarity weight
innerPredict_TSW = np.zeros(
sourceGraph[train_index].shape[1])[np.newaxis]
#summing up the best neighbors
for j1 in range(0, TS_bestNb):
tr = (rearrangedTargetView[:, Index_ALL[tSubjectIndex,
j1]])[np.newaxis]
if j1 == 0:
innerPredict_TSW = innerPredict_TSW.T + tr.T * newWeight_TSW[
j1]
else:
innerPredict_TSW = innerPredict_TSW + tr.T * newWeight_TSW[
j1]
# scale weight to 1
Scale_TSW = sum(newWeight_TSW)
innerPredict_TSW = np.divide(innerPredict_TSW, Scale_TSW)
# calculate result (MAE)
tr2 = (rearrangedTargetView[:, tSubjectIndex])[np.newaxis]
resulttsw = abs(tr2.T - innerPredict_TSW)
iMAE_TSW = mean_absolute_error(tr2.T, innerPredict_TSW)
overallResult_TSW[tSubjectOriginalIndex,
TS_bestNb] = overallResult_TSW[
tSubjectOriginalIndex, TS_bestNb] + iMAE_TSW
allSV = np.append(allSV, sourceGraph[test_index], axis=0)
testlabel.append(labels[test_index])
allpredTV = np.append(allpredTV, innerPredict_TSW.T, axis=0)
print test_index
dataset_source_and_predicted_target = np.concatenate((allSV, allpredTV),
axis=1)
print('END')
mae = np.mean(overallResult_TSW, axis=0)
print("Mean Absolute Error: ")
print(mae[np.nonzero(mae)])
return mae, dataset_source_and_predicted_target, testlabel
mu, sigma = 0.22266368090882432, 0.027202072213276744 # mean and standard deviation
sourceGraph = np.random.normal(mu, sigma, (150, 595))
mu, sigma = 0.08308065685993601, 0.01338490182696101
targetGraph = np.random.normal(mu, sigma, (150, 595))
labels = np.concatenate((np.zeros((1, 75)), np.ones((1, 75))), axis=None)
## HADA execution
model = 'arga_ae' #autoencoder/variational autoencoder
settings = settings.get_settings_new(model)
mae, dataset_source_and_predicted_target, testlabel = HADA(
sourceGraph, targetGraph, labels, settings)
## STEP 3: Disease Classification using Random Forest
classes = testlabel
label = np.array(classes)
loo = LeaveOneOut()
actual_label = []
predicted_sv_predtv_label = []
RF = RandomForestClassifier(bootstrap=False,
class_weight=None,
criterion='gini',
max_depth=None,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=400,
header = data_table[0]
del data_table[0]
data = np.zeros((len(data_table), len(data_table[0])))
for i in range(0,len(data_table)):
tmp = data_table[i]
for j in range(0,len(tmp)):
data[i,j] = float(tmp[j])
feature=data[:,[1,2,3,4]]
labels=data[:,[0]]
# Perform Leave One Out Validation on just the Decision Tree Classifier.
LOO=LeaveOneOut()
number_of_iterations=LOO.get_n_splits(feature)
total_score=0;
d3=tree.DecisionTreeClassifier()
for train_index,test_index in LOO.split(feature):
#print("TRAIN:", train_index, "TEST:", test_index)
train_features, test_features = feature[train_index], feature[test_index]
train_labels, test_labels = labels[train_index], labels[test_index]
d3=tree.DecisionTreeClassifier()
clf=d3.fit(train_features,train_labels)
total_score+=clf.score(test_features,test_labels)
score = mean_score=(total_score/number_of_iterations)
print("D3 + leave one cross validation:",score)
def findBestAlpha(data, x_cols, y_cols, parameters, alphas):
"""
Best alpha value for MLP
Arguments:
data {array} -- Data
x_cols {arrray} -- x columns
y_cols {array} -- y columns
parameters {namedTuple} -- parameters for the classifier
alphas {array} -- array of alphas to test
"""
best_alpha = 0
best_accu = 0
x = data.loc[:, x_cols]
y = data.loc[:, y_cols]
#Picking best k
for a in alphas:
loo = LeaveOneOut()
loo.get_n_splits(data)
n = loo.split(data)
mlpClassifier = MLPClassifier(
hidden_layer_sizes=parameters.hidden_layer_sizes,
solver=parameters.solver,
alpha=a,
batch_size=parameters.batch_size,
learning_rate=parameters.learning_rate,
learning_rate_init=parameters.learning_rate_init,
max_iter=parameters.max_iter,
random_state=parameters.random_state,
verbose=parameters.verbose,
early_stopping=parameters.early_stopping,
validation_fraction=parameters.validation_fraction)
accuracy_a = []
real_label = []
pred_label = []
for train_index, test_index in n: #Each row is test data once
xtrain, xtest = x.iloc[train_index], x.iloc[test_index]
ytrain, ytest = y.iloc[train_index], y.iloc[test_index]
mlpClassifier.fit(xtrain, ytrain.values.ravel())
ypred = mlpClassifier.predict(xtest)
pred_label.append(ypred)
real_label.append(ytest)
acc = accuracy_score(ytest, ypred)
accuracy_a.append(acc)
avg_acc = np.mean(accuracy_a)
print(a, ": average accuracy ", avg_acc)
if (avg_acc > best_accu): #Updating best_k if accuracy is better
best_accu = avg_acc
best_alpha = a
print("Best alpha=", best_alpha)
print("Best accuracy=", best_accu)
return (best_alpha)
def testMlp(data, parameters, x_cols, y_cols, plots=False):
"""
testing MLP classifier
Arguments:
data {array} -- Data
x_cols {array} -- x columns
y_cols {array} -- y columns
Keyword Arguments:
plots {bool} -- Used for plotting (default: {False})
"""
x = data.loc[:, x_cols]
y = data.loc[:, y_cols]
loo = LeaveOneOut()
loo.get_n_splits(data)
n = loo.split(data)
mlpClassifier = MLPClassifier(
hidden_layer_sizes=parameters.hidden_layer_sizes,
solver=parameters.solver,
alpha=parameters.alpha,
batch_size=parameters.batch_size,
learning_rate=parameters.learning_rate,
learning_rate_init=parameters.learning_rate_init,
max_iter=parameters.max_iter,
random_state=parameters.random_state,
verbose=parameters.verbose,
early_stopping=parameters.early_stopping,
validation_fraction=parameters.validation_fraction)
accuracy_a = []
real_label = []
pred_label = []
for train_index, test_index in n: #Each row is test data once
xtrain, xtest = x.iloc[train_index], x.iloc[test_index]
ytrain, ytest = y.iloc[train_index], y.iloc[test_index]
mlpClassifier.fit(xtrain, ytrain.values.ravel())
ypred = mlpClassifier.predict(xtest)
pred_label.append(ypred)
real_label.append(ytest.values)
acc = accuracy_score(ytest, ypred)
accuracy_a.append(acc)
avg_acc = np.mean(accuracy_a)
pred_label_df = pd.DataFrame(columns=["label"])
real_label_df = pd.DataFrame(columns=["label"])
#Forming the dataframes
for row in range(0, len(pred_label)):
label_str = pred_label[row][0]
pred_label_df.loc[row] = label_str
for row in range(0, len(real_label)):
label_str = real_label[row][0][0]
real_label_df.loc[row] = label_str
if (plots):
cm = confusion_matrix(real_label_df, pred_label_df)
cm_df = pd.DataFrame(cm, ["Fall", "Normal"], ["Fall", "Normal"])
sn.set(font_scale=1.5)
sn.heatmap(cm_df, annot=True, annot_kws={"size": 32}, fmt='d')
plt.savefig("../figs/svm_heatmap.png",
facecolor="w",
bbox_inches="tight")
plt.show()
#Checking accuracy
print("SVM average accuracy: ", round(avg_acc, 2)) #2 decimals
#More detailed report
print(classification_report(real_label_df, pred_label_df))
return (avg_acc, real_label_df, pred_label_df)
def pca_graph_pvals_less_than():
data = preproccessed_data.join(mapping_file[[
'Age', 'BMI', 'FattyLiver', 'RegularExercise', 'Smoking',
'DiagnosisGroup'
]])
X = data.drop([
'Age', 'BMI', 'FattyLiver', 'RegularExercise', 'Smoking',
'DiagnosisGroup'
],
axis=1)
y = data['DiagnosisGroup']
for n_comp in range(2, 30):
pcas.append(n_comp)
loo = LeaveOneOut()
y_pred_list = []
auc = []
auc_train = []
for train_index, test_index in loo.split(X):
train_index = list(train_index)
# print("%s %s" % (train_index, test_index))
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y[train_index], y[test_index]
most_corelated_taxon = {}
for i in range(X_train.shape[1]):
p_val = scipy.stats.spearmanr(X_train.iloc[:, i], y_train)[1]
if math.isnan(p_val):
most_corelated_taxon[X_train.columns[i]] = 1
else:
most_corelated_taxon[X_train.columns[i]] = p_val
sorted_taxon = sorted(most_corelated_taxon.items(),
key=operator.itemgetter(1))
most_corelated_taxon = [i for i in sorted_taxon if i[1] <= 0.01]
bact = [i[0] for i in most_corelated_taxon if i[0] != 1]
new_data = X[bact]
otu_after_pca, _ = apply_pca(new_data, n_components=n_comp)
new_data = otu_after_pca.join(data[[
'Age', 'BMI', 'FattyLiver', 'RegularExercise', 'Smoking',
'DiagnosisGroup'
]],
how='inner')
X_new = new_data.drop(['DiagnosisGroup'], axis=1)
y_new = new_data['DiagnosisGroup']
regex = re.compile(r"\[|\]|<", re.IGNORECASE)
X_new.columns = [
regex.sub("_", col) if any(x in str(col)
for x in set(('[', ']',
'<'))) else col
for col in X_new.columns.values
]
X_train, X_test = X_new.iloc[train_index], X_new.iloc[test_index]
y_train, y_test = y_new[train_index], y_new[test_index]
model = XGBClassifier(max_depth=4,
n_estimators=150,
learning_rate=15 / 100,
objective='multi:softmax')
#objective='binary:logistic',
#scale_pos_weight=(np.sum(y_train == -1) / np.sum(y_train == 1)))
model.fit(X_train, y_train)
pred_train = model.predict(X_train)
auc_train.append(metrics.accuracy_score(y_train, pred_train))
y_pred = model.predict(X_test)
y_pred_list.append(y_pred[0])
try:
auc = metrics.accuracy_score(y, y_pred_list)
except:
pass
print('PCA components' + str(n_comp), round(auc, 2))
scores = round(auc, 2)
scores_train = round(np.array(auc_train).mean(), 2)
train_accuracy.append(scores_train)
test_accuracy.append(round(scores.mean(), 2))
parameters = {'alpha': [1e-15, 1e-10, 1e-8, 1e-4, 1e-3,1e-2, 1, 5, 10, 20]}
lasso_regressor = GridSearchCV(lasso, parameters, scoring='neg_mean_squared_error', cv = 16)
lasso_regressor.fit(X, y)
lasso_regressor.best_params_
lasso_regressor.best_score_
# performs worse compared to linear regression
##
#%% try with leave one out
import numpy as np
from sklearn.model_selection import LeaveOneOut
loo = LeaveOneOut()
#loo.get_n_splits(X)
loo.get_n_splits(np.array(subject_list[0:18])[mask])
# chose subject number
t1 = creatSubCor(subject_list, '1', 'speficTrial_0_trauma.npy')
df_clinical.describe()
X = np.array(subjectPosEdges[0:18])[mask].reshape(-1,1)
y = np.array(diffPCL)[mask]#.reshape(-1,1)
print(loo)
for train_index, test_index in loo.split(np.array(subject_list[0:18])[mask]):
# should insert matrx tresholding for all subjects picked and calculating (each iteration) the Sum of Positive edges
print(np.array(subject_list)[train_index])
print("TRAIN:", train_index, "TEST:", test_index)
def lda_project(spike_times,
spike_clusters,
event_times,
event_groups,
pre_time=0,
post_time=0.5,
cross_validation='kfold',
num_splits=5,
prob_left=None,
custom_validation=None):
"""
Use linear discriminant analysis to project population vectors to the line that best separates
the two groups. When cross-validation is used, the LDA projection is fitted on the training
data after which the test data is projected to this projection.
spike_times : 1D array
spike times (in seconds)
spike_clusters : 1D array
cluster ids corresponding to each event in `spikes`
event_times : 1D array
times (in seconds) of the events from the two groups
event_groups : 1D array
group identities of the events, can be any number of groups, accepts integers and strings
cross_validation : string
which cross-validation method to use, options are:
'none' No cross-validation
'kfold' K-fold cross-validation
'leave-one-out' Leave out the trial that is being decoded
'block' Leave out the block the to-be-decoded trial is in
'custom' Any custom cross-validation provided by the user
num_splits : integer
** only for 'kfold' cross-validation **
Number of splits to use for k-fold cross validation, a value of 5 means that the decoder
will be trained on 4/5th of the data and used to predict the remaining 1/5th. This process
is repeated five times so that all data has been used as both training and test set.
prob_left : 1D array
** only for 'block' cross-validation **
the probability of the stimulus appearing on the left for each trial in event_times
custom_validation : generator
** only for 'custom' cross-validation **
a generator object with the splits to be used for cross validation using this format:
(
(split1_train_idxs, split1_test_idxs),
(split2_train_idxs, split2_test_idxs),
(split3_train_idxs, split3_test_idxs),
...)
n_neurons : int
Group size of number of neurons to be sub-selected
Returns
-------
lda_projection : 1D array
the position along the LDA projection axis for the population vector of each trial
"""
# Check input
assert cross_validation in [
'none', 'kfold', 'leave-one-out', 'block', 'custom'
]
assert event_times.shape[0] == event_groups.shape[0]
if cross_validation == 'block':
assert event_times.shape[0] == prob_left.shape[0]
if cross_validation == 'custom':
assert isinstance(custom_validation, types.GeneratorType)
# Get matrix of all neuronal responses
times = np.column_stack(
((event_times - pre_time), (event_times + post_time)))
pop_vector, cluster_ids = get_spike_counts_in_bins(spike_times,
spike_clusters, times)
pop_vector = pop_vector.T
# Initialize
lda = LinearDiscriminantAnalysis()
lda_projection = np.zeros(event_groups.shape)
if cross_validation == 'none':
# Find the best LDA projection on all data and transform those data
lda_projection = lda.fit_transform(pop_vector, event_groups)
else:
# Perform cross-validation
if cross_validation == 'leave-one-out':
cv = LeaveOneOut().split(pop_vector)
elif cross_validation == 'kfold':
cv = KFold(n_splits=num_splits).split(pop_vector)
elif cross_validation == 'block':
block_lengths = [sum(1 for i in g) for k, g in groupby(prob_left)]
blocks = np.repeat(np.arange(len(block_lengths)), block_lengths)
cv = LeaveOneGroupOut().split(pop_vector, groups=blocks)
elif cross_validation == 'custom':
cv = custom_validation
# Loop over the splits into train and test
for train_index, test_index in cv:
# Find LDA projection on the training data
lda.fit(pop_vector[train_index],
[event_groups[j] for j in train_index])
# Project the held-out test data to projection
lda_projection[test_index] = lda.transform(
pop_vector[test_index]).T[0]
return lda_projection
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from sklearn.datasets import load_iris
from sklearn.model_selection import LeaveOneOut
data, classifier = load_iris(return_X_y=True)
data = np.delete(data, 0, 1)
data = np.delete(data, 0, 1)
score = np.zeros([len(data)])
acc = np.zeros([len(data)])
loocv = LeaveOneOut()
for train_index, test_index in loocv.split(data):
Xtrain, Xtest = data[train_index], data[test_index]
Ytrain, Ytest = classifier[train_index], classifier[test_index]
#holdout = 0.2;
#Xtrain, Xtest, Ytrain, Ytest = train_test_split(data, classifier, test_size=holdout)
model = Sequential()
model.add(Dense(30, activation='sigmoid', input_shape=(2, )))
model.add(Dense(30, activation='sigmoid'))
model.add(Dense(1))
sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error',
optimizer=sgd,
def allergies_distance_matrix(distance='spearman', clustering='spectral'):
for i in range(0, df.shape[1]):
for j in range(0, df.shape[1]):
#Spearman correlation
if distance == 'spearman':
dist_mat.at[df.columns[i], df.columns[j]] = abs(
round(
scipy.stats.spearmanr(
np.array(df.iloc[:, i]).astype(float),
np.array(df.iloc[:, j]).astype(float))[0], 4))
#Euclidean distance
else:
dist_mat.at[df.columns[i], df.columns[j]] = np.linalg.norm(
np.array(df.iloc[:, i]).astype(float) -
np.array(df.iloc[:, j]).astype(float))
if clustering == 'spectral':
clustering = SpectralClustering(n_clusters=2,
affinity='precomputed',
assign_labels='discretize',
random_state=0)
else:
clustering = AgglomerativeClustering(affinity='precomputed',
linkage='average')
clustering.fit(dist_mat.values)
bact_label1 = []
bact_label0 = []
bact_label = {0: [], 1: []}
for i in range(0, df.shape[1]):
if clustering.labels_[i] == 1:
bact_label1.append(df.columns[i])
else:
bact_label0.append(df.columns[i])
bact_label_name = {0: [], 1: []}
bact_label_tmp = {0: [], 1: []}
bact_level = level - 1
for k in [0, 1]:
for i in bact_label[k]:
for key, value in dict_bact.items():
for j in value:
if i == j:
bact_label_tmp[k].append(key)
bact_label_tmp[k] = set(bact_label_tmp[k])
for i in bact_label_tmp[k]:
if i != 'else':
for j in taxonomy:
try:
if j.split(';')[bact_level] == i:
bact_label_name[k].append(','.join(
j.split(';')[0:bact_level + 1]))
break
except:
continue
else:
bact_label_name[k].append('else')
bact_label_name[k] = set(bact_label_name[k])
df1 = df[bact_label1]
df0 = df[bact_label0]
pca = PCA(n_components=min(round(df0.shape[1] / 2) + 1, df0.shape[0]))
pca.fit(df0)
sum = 0
num_comp = 0
for (i, component) in enumerate(pca.explained_variance_ratio_):
if sum <= 0.5:
sum += component
else:
num_comp = i
break
if num_comp == 0:
num_comp += 1
otu_after_pca0, _ = apply_pca(df0, n_components=num_comp, print_data=False)
merged_data0 = otu_after_pca0.join(mapping_file)
X = merged_data0.drop(['disease'], axis=1)
y = merged_data0['disease']
loo = LeaveOneOut()
accuracy = []
y_pred_list = []
for train_index, test_index in loo.split(X):
train_index = list(train_index)
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index, :]
y_train, y_test = y[train_index], y[test_index]
model = XGBClassifier(max_depth=5,
n_estimators=300,
learning_rate=15 / 100,
objective='binary:logistic',
scale_pos_weight=(np.sum(y_train == 0) /
np.sum(y_train == 1)),
reg_lambda=450)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_pred_list.append(y_pred)
y_pred_train = model.predict(X_train)
print('Train Precision: ' +
str(round(precision_score(y_train, y_pred_train), 2)))
print('Train Recall: ' +
str(round(recall_score(y_train, y_pred_train), 2)))
cnf_matrix = metrics.confusion_matrix(y_train, y_pred_train)
class_names = ['Control', 'GVHD']
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=list(class_names),
normalize=True,
title='Normalized confusion matrix')
plt.show()
print('Precision: ' + str(round(precision_score(y, y_pred_list), 2)))
print('Recall: ' + str(round(recall_score(y, y_pred_list), 2)))
cnf_matrix = metrics.confusion_matrix(y, y_pred_list)
# # Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=list(class_names),
normalize=True,
title='Normalized confusion matrix')
plt.show()
#
pca = PCA(n_components=min(round(df1.shape[1] / 2) + 1, df1.shape[0]))
pca.fit(df1)
sum = 0
num_comp = 0
for (i, component) in enumerate(pca.explained_variance_ratio_):
if sum <= 0.5:
sum += component
else:
num_comp = i
break
if num_comp == 0:
num_comp += 1
otu_after_pca1, _ = apply_pca(df1, n_components=num_comp, print_data=False)
merged_data1 = otu_after_pca1.join(mapping_file)
X = merged_data1.drop(['disease'], axis=1)
y = merged_data1['disease']
loo = LeaveOneOut()
accuracy = []
y_pred_list = []
for train_index, test_index in loo.split(X):
train_index = list(train_index)
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index, :]
y_train, y_test = y[train_index], y[test_index]
model = XGBClassifier(max_depth=5,
n_estimators=300,
learning_rate=15 / 100,
objective='binary:logistic',
scale_pos_weight=(np.sum(y_train == 0) /
np.sum(y_train == 1)),
reg_lambda=450)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_pred_list.append(y_pred)
y_pred_train = model.predict(X_train)
print('Train Precision: ' +
str(round(precision_score(y_train, y_pred_train), 2)))
print('Train Recall: ' +
str(round(recall_score(y_train, y_pred_train), 2)))
cnf_matrix = metrics.confusion_matrix(y_train, y_pred_train)
class_names = ['Control', 'GVHD']
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=list(class_names),
normalize=True,
title='Normalized confusion matrix')
plt.show()
print('Precision: ' + str(round(precision_score(y, y_pred_list), 2)))
print('Recall: ' + str(round(recall_score(y, y_pred_list), 2)))
cnf_matrix = metrics.confusion_matrix(y, y_pred_list)
# # Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix,
classes=list(class_names),
normalize=True,
title='Normalized confusion matrix')
plt.show()
def fit(self, X, y):
"""Fit the model using X as training data and y as target values
Parameters
----------
X : sktime-format pandas dataframe with shape([n_cases,n_dimensions]),
or numpy ndarray with shape([n_cases,n_readings,n_dimensions])
y : {array-like, sparse matrix}
Target values of shape = [n_samples]
"""
X, y = check_X_y(X, y, enforce_univariate=True)
y = np.asarray(y)
X = np.array(
[np.asarray([x]).reshape(len(x), 1) for x in X.iloc[:, 0]])
check_classification_targets(y)
# if internal cv is desired, the relevant flag forces a grid search
# to evaluate the possible values,
# find the best, and then set this classifier's params to match
if self._cv_for_params:
grid = GridSearchCV(estimator=KNeighborsTimeSeriesClassifier(
metric=self.metric, n_neighbors=1, algorithm="brute"),
param_grid=self._param_matrix,
cv=LeaveOneOut(),
scoring='accuracy')
grid.fit(X, y)
self.metric_params = grid.best_params_['metric_params']
if y.ndim == 1 or y.ndim == 2 and y.shape[1] == 1:
if y.ndim != 1:
warnings.warn(
"A column-vector y was passed when a 1d array "
"was expected. Please change the shape of y to "
"(n_samples, ), for example using ravel().",
DataConversionWarning,
stacklevel=2)
self.outputs_2d_ = False
y = y.reshape((-1, 1))
else:
self.outputs_2d_ = True
self.classes_ = []
self._y = np.empty(y.shape, dtype=np.int)
for k in range(self._y.shape[1]):
classes, self._y[:, k] = np.unique(y[:, k], return_inverse=True)
self.classes_.append(classes)
if not self.outputs_2d_:
self.classes_ = self.classes_[0]
self._y = self._y.ravel()
temp = check_array.__wrapped__.__code__
check_array.__wrapped__.__code__ = _check_array_ts.__code__
fx = self._fit(X)
check_array.__wrapped__.__code__ = temp
self._is_fitted = True
return fx
def testTreePredictions(data, parameters, x_cols, y_cols, plots=False):
"""
Testing tree prediction accuracies.
Arguments:
data {array} -- Labeled data for classifier testing.
x_cols {array} -- x columns
y_cols {array} -- y columns
parameters {namedtuple} -- Parameters for the tree classifier. Using named tuple to keep things tidy.
Keyword Arguments:
plots {bool} -- Used for plotting (default: {False})
"""
x = data.loc[:, x_cols]
y = data.loc[:, y_cols]
loo = LeaveOneOut()
loo.get_n_splits(data)
n = loo.split(data)
#Creating the classifier with the input parameters
treeClassifier = tree.DecisionTreeClassifier(
class_weight=parameters.class_weight,
criterion=parameters.criterion,
max_depth=parameters.max_depth,
max_features=parameters.max_features,
max_leaf_nodes=parameters.max_leaf_nodes,
min_samples_leaf=parameters.min_samples_leaf,
min_samples_split=parameters.min_samples_split,
min_weight_fraction_leaf=parameters.min_weight_fraction_leaf,
presort=parameters.presort,
random_state=parameters.random_state,
splitter=parameters.splitter)
accuracy_a = []
real_label = []
pred_label = []
for train_index, test_index in n: #Each row is test data once
xtrain, xtest = x.iloc[train_index], x.iloc[test_index]
ytrain, ytest = y.iloc[train_index], y.iloc[test_index]
#Fitting train data
treeClassifier = treeClassifier.fit(xtrain, ytrain)
#Predictions
ypred = treeClassifier.predict(xtest)
pred_label.append(ypred)
real_label.append(ytest.values)
#Accuracy
acc = accuracy_score(ytest, ypred)
accuracy_a.append(acc)
pred_label_df = pd.DataFrame(columns=["label"])
real_label_df = pd.DataFrame(columns=["label"])
#Forming the dataframes
for row in range(0, len(pred_label)):
label_str = pred_label[row][0]
pred_label_df.loc[row] = label_str
for row in range(0, len(real_label)):
label_str = real_label[row][0][0]
real_label_df.loc[row] = label_str
if (plots): #Plotting tree and accuracy heatmap
#not found in the library for some reason, currently using old version?
#plt.figure(figsize=[12, 12])
#tree.plot_tree(treeClassifier, filled=True)
#plt.show()
#Workaround attempt for tree plotting
dot = io.StringIO()
tree.export_graphviz(treeClassifier, out_file=dot)
(graph, ) = pydot.graph_from_dot_data(dot.getvalue())
graph.write_png("../figs/treeClassifier.png")
cm = confusion_matrix(real_label_df, pred_label_df)
cm_df = pd.DataFrame(cm, ["Fall", "Normal"], ["Fall", "Normal"])
sn.set(font_scale=1.5)
sn.heatmap(cm_df, annot=True, annot_kws={"size": 32}, fmt='d')
plt.savefig("../figs/tree_heatmap.png",
facecolor="w",
bbox_inches="tight")
plt.show()
avg_acc = np.mean(accuracy_a)
#Checking accuracy
print("Tree average accuracy: ", round(avg_acc, 2)) #2 decimals
#More detailed report
print(classification_report(real_label_df, pred_label_df))
return (avg_acc, real_label_df, pred_label_df)
#Iris data cross-validation
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import LeaveOneOut
from sklearn import datasets
import numpy as np
import pandas as pd
#tmp=pd.read_csv('iris.data',sep=',')
#iris=np.loadtxt('iris.data', delimiter=',')
iris=datasets.load_iris()
x=iris['data'][0:149]
y=iris['target'][0:149]
log_model=LogisticRegression()
m=np.shape(x)[0]
y_pred=cross_val_predict(log_model,x,y,cv=10)
print(metrics.accuracy_score(y,y_pred))
#print(y_pred)
loo=LeaveOneOut()
accuracy=0
for train,test in loo.split(x):
log_model.fit(x[train],y[train])
y_pred1=log_model.predict(x[test])
if y_pred1==y[test]:accuracy+=1
print (accuracy/m)
def _cost_fn(argd, X, y, EX_list, valid_size, n_folds, shuffle, random_state,
use_partial_fit, info, timeout, _conn, loss_fn=None,
continuous_loss_fn=False, best_loss=None, n_jobs=1):
'''Calculate the loss function
'''
try:
t_start = time.time()
# Extract info from calling function.
if 'classifier' in argd:
classifier = argd['classifier']
regressor = argd['regressor']
preprocessings = argd['preprocessing']
ex_pps_list = argd['ex_preprocs']
else:
classifier = argd['model']['classifier']
regressor = argd['model']['regressor']
preprocessings = argd['model']['preprocessing']
ex_pps_list = argd['model']['ex_preprocs']
learner = classifier if classifier is not None else regressor
# Set n_jobs parameter if available for given learner
if hasattr(learner, 'n_jobs'):
# https://github.com/hyperopt/hyperopt-sklearn/issues/82#issuecomment-430963445
learner.n_jobs = n_jobs
is_classif = classifier is not None
untrained_learner = copy.deepcopy(learner)
# -- N.B. modify argd['preprocessing'] in-place
# Determine cross-validation iterator.
if n_folds is not None:
if n_folds == -1:
info('Will use leave-one-out CV')
try:
cv_iter = LeaveOneOut().split(X)
except TypeError:
# Older syntax before sklearn version 0.18
cv_iter = LeaveOneOut(len(y))
elif is_classif:
info('Will use stratified K-fold CV with K:', n_folds,
'and Shuffle:', shuffle)
try:
cv_iter = StratifiedKFold(n_splits=n_folds,
shuffle=shuffle,
random_state=random_state
).split(X, y)
except TypeError:
# Older syntax before sklearn version 0.18
cv_iter = StratifiedKFold(y, n_folds=n_folds,
shuffle=shuffle,
random_state=random_state)
else:
info('Will use K-fold CV with K:', n_folds,
'and Shuffle:', shuffle)
try:
cv_iter = KFold(n_splits=n_folds,
shuffle=shuffle,
random_state=random_state).split(X)
except TypeError:
# Older syntax before sklearn version 0.18
cv_iter = KFold(len(y), n_folds=n_folds,
shuffle=shuffle,
random_state=random_state)
else:
if not shuffle: # always choose the last samples.
info('Will use the last', valid_size,
'portion of samples for validation')
n_train = int(len(y) * (1 - valid_size))
valid_fold = np.ones(len(y), dtype=np.int)
valid_fold[:n_train] = -1 # "-1" indicates train fold.
try:
cv_iter = PredefinedSplit(valid_fold).split()
except TypeError:
# Older syntax before sklearn version 0.18
cv_iter = PredefinedSplit(valid_fold)
elif is_classif:
info('Will use stratified shuffle-and-split with validation \
portion:', valid_size)
try:
cv_iter = StratifiedShuffleSplit(1, test_size=valid_size,
random_state=random_state
).split(X, y)
except TypeError:
# Older syntax before sklearn version 0.18
cv_iter = StratifiedShuffleSplit(y, 1, test_size=valid_size,
random_state=random_state)
else:
info('Will use shuffle-and-split with validation portion:',
valid_size)
try:
cv_iter = ShuffleSplit(n_splits=1, test_size=valid_size,
random_state=random_state).split(X)
except TypeError:
# Older syntax before sklearn version 0.18
cv_iter = ShuffleSplit(len(y), 1, test_size=valid_size,
random_state=random_state)
# Use the above iterator for cross-validation prediction.
cv_y_pool = np.array([])
cv_pred_pool = np.array([])
cv_n_iters = np.array([])
for train_index, valid_index in cv_iter:
Xfit, Xval = X[train_index], X[valid_index]
yfit, yval = y[train_index], y[valid_index]
if EX_list is not None:
_EX_list = [ (EX[train_index], EX[valid_index])
for EX in EX_list ]
EXfit_list, EXval_list = zip(*_EX_list)
else:
EXfit_list = None
EXval_list = None
XEXfit, XEXval = transform_combine_XEX(
Xfit, info, preprocessings, Xval,
EXfit_list, ex_pps_list, EXval_list
)
learner = copy.deepcopy(untrained_learner)
info('Training learner', learner, 'on X/EX of dimension',
XEXfit.shape)
if hasattr(learner, "partial_fit") and use_partial_fit:
learner, n_iters = pfit_until_convergence(
learner, is_classif, XEXfit, yfit, info,
best_loss=best_loss, XEXval=XEXval, yval=yval,
timeout=timeout, t_start=t_start
)
else:
learner.fit(XEXfit, yfit)
n_iters = None
if learner is None:
break
cv_y_pool = np.append(cv_y_pool, yval)
info('Scoring on X/EX validation of shape', XEXval.shape)
if continuous_loss_fn:
cv_pred_pool = np.append(cv_pred_pool, learner.predict_proba(XEXval))
else:
cv_pred_pool = np.append(cv_pred_pool, learner.predict(XEXval))
cv_n_iters = np.append(cv_n_iters, n_iters)
else: # all CV folds are exhausted.
if loss_fn is None:
if is_classif:
loss = 1 - accuracy_score(cv_y_pool, cv_pred_pool)
# -- squared standard error of mean
lossvar = (loss * (1 - loss)) / max(1, len(cv_y_pool) - 1)
info('OK trial with accuracy %.1f +- %.1f' % (
100 * (1 - loss),
100 * np.sqrt(lossvar))
)
else:
loss = 1 - r2_score(cv_y_pool, cv_pred_pool)
lossvar = None # variance of R2 is undefined.
info('OK trial with R2 score %.2e' % (1 - loss))
else:
# Use a user specified loss function
loss = loss_fn(cv_y_pool, cv_pred_pool)
lossvar = None
info('OK trial with loss %.1f' % loss)
t_done = time.time()
rval = {
'loss': loss,
'loss_variance': lossvar,
'learner': untrained_learner,
'preprocs': preprocessings,
'ex_preprocs': ex_pps_list,
'status': hyperopt.STATUS_OK,
'duration': t_done - t_start,
'iterations': (cv_n_iters.max()
if (hasattr(learner, "partial_fit") and use_partial_fit)
else None),
}
rtype = 'return'
# The for loop exit with break, one fold did not finish running.
if learner is None:
t_done = time.time()
rval = {
'status': hyperopt.STATUS_FAIL,
'failure': 'Not enough time to finish training on \
all CV folds',
'duration': t_done - t_start,
}
rtype = 'return'
##==== Cost function exception handling ====##
except (NonFiniteFeature,) as exc:
print('Failing trial due to NaN in', str(exc))
t_done = time.time()
rval = {
'status': hyperopt.STATUS_FAIL,
'failure': str(exc),
'duration': t_done - t_start,
}
rtype = 'return'
except (ValueError,) as exc:
if ('k must be less than or equal'
' to the number of training points') in str(exc):
t_done = time.time()
rval = {
'status': hyperopt.STATUS_FAIL,
'failure': str(exc),
'duration': t_done - t_start,
}
rtype = 'return'
else:
rval = exc
rtype = 'raise'
except (AttributeError,) as exc:
print('Failing due to k_means_ weirdness')
if "'NoneType' object has no attribute 'copy'" in str(exc):
# -- sklearn/cluster/k_means_.py line 270 raises this sometimes
t_done = time.time()
rval = {
'status': hyperopt.STATUS_FAIL,
'failure': str(exc),
'duration': t_done - t_start,
}
rtype = 'return'
else:
rval = exc
rtype = 'raise'
except Exception as exc:
rval = exc
rtype = 'raise'
# -- return the result to calling process
_conn.send((rtype, rval))
def computeCVROC(df, model, outcomeVar, predVars, nFolds=10, LOO=False):
"""Apply model to df and return performance metrics in a cross-validation framework.
Parameters
----------
df : pd.DataFrame
Must contain outcome and predictor variables.
model : sklearn or other model
Model must have fit and predict methods.
outcomeVar : str
predVars : ndarray or list
Predictor variables in the model.
nFolds : int
N-fold cross-validation (not required for LOO)
Returns
-------
fpr : np.ndarray
Pre-specified vector of FPR thresholds for interpolation
fpr = np.linspace(0, 1, 100)
meanTPR : np.ndarray
Mean true-positive rate in test fraction.
auc : float
Area under the mean ROC curve.
acc : float
Mean accuracy score in test fraction.
results : returned by model.fit()
Training model results object for each fold
prob : pd.Series
Mean predicted probabilities on test data with index from df
success : bool
An indicator of whether the cross-validation was completed."""
if not isinstance(predVars, list):
predVars = list(predVars)
tmp = df[[outcomeVar] + predVars].dropna()
X,y = tmp[predVars].astype(float), tmp[outcomeVar].astype(float)
if LOO:
cv = LeaveOneOut()
nFolds = cv.get_n_splits(y)
cv_iter = cv.split(y=y)
else:
cv = StratifiedKFold(n_splits=nFolds, shuffle=True)
cv_iter = cv.split(X=X, y=y)
fpr = np.linspace(0, 1, 100)
tpr = np.nan * np.zeros((fpr.shape[0], nFolds))
acc = np.nan * np.zeros(nFolds)
auc = np.nan * np.zeros(nFolds)
coefs = []
probs = []
for outi, (trainInd, testInd) in enumerate(cv_iter):
Xtrain, Xtest = X.iloc[trainInd], X.iloc[testInd]
ytrain, ytest = y.iloc[trainInd], y.iloc[testInd]
results = model.fit(X=Xtrain, y=ytrain)
prob = results.predict_proba(Xtest)
class1Ind = np.nonzero(results.classes_ == 1)[0][0]
fprTest, tprTest, _ = sklearn.metrics.roc_curve(ytest, prob[:, class1Ind])
tpr[:, outi] = np.interp(fpr, fprTest, tprTest)
auc[outi] = sklearn.metrics.auc(fprTest, tprTest)
acc[outi] = sklearn.metrics.accuracy_score(ytest, np.round(prob[:, class1Ind]), normalize=True)
coefs.append(results.coef_[None,:])
probs.append(pd.Series(prob[:, class1Ind], index=Xtest.index))
meanTPR = np.mean(tpr, axis=1)
meanTPR[0], meanTPR[-1] = 0, 1
meanACC = np.mean(acc)
meanAUC = sklearn.metrics.auc(fpr, meanTPR)
"""Compute mean probability over test predictions in CV"""
probS = pd.concat(probs).groupby(level=0).agg(np.mean)
probS.name = 'Prob'
"""Refit all the data for final model"""
result = model.fit(X=X, y=y)
rocRes = rocStats(y, np.round(probS))
outD = {'fpr':fpr, # (100, ) average FPR for ROC
'tpr':meanTPR, # (100, ) average TPR for ROC
'AUC':auc, # (CVfolds, ) AUC of ROC for each outer test fold
'mAUC': meanAUC, # (1, ) AUC of the average ROC
'mACC': np.mean(acc),
'ACC':acc, # (CVfolds, ) accuracy across outer test folds
'finalResult': result, # final fitted model with predict() exposed
'prob':probS, # (N,) pd.Series of predicted probabilities avg over outer folds
'coefs':np.concatenate(coefs), # (CVfolds, predVars)
'Xvars':predVars,
'Yvar':outcomeVar,
'nFolds':nFolds,
'LOO':'Yes' if LOO else 'No',
'N':tmp.shape[0]}
outD.update(rocRes[['Sensitivity', 'Specificity']].to_dict())
return outD
import pandas as pd
TimeSeriesSplit)
data = list(range(1, 11))
print(data)
print(train_test_split(data, train_size=.8))
kf = KFold(n_splits=5)
for train, validate in kf.split(data):
print(train, validate)
kf = KFold(n_splits=5, shuffle=True, random_state=42)
for train, validate in kf.split(data):
print(train, validate)
loo = LeaveOneOut()
for train, validate in loo.split(data):
print(train, validate)
lpo = LeavePOut(p=2)
for train, validate in lpo.split(data):
print(train, validate)
ss = ShuffleSplit(n_splits=3, test_size=2, random_state=0)
for train, validate in ss.split(data):
print(train, validate)
tscv = TimeSeriesSplit(n_splits=5)
for train, validate in tscv.split(data):
print(train, validate)
from sklearn import datasets
from sklearn.neighbors import KNeighborsClassifier
iris = datasets.load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier()
# ==== Leave-one-out validation ====
from sklearn.model_selection import LeaveOneOut
# Instatiate `LeaveOneOut` class. See [here](http://scikit-learn.org/stable/modules/cross_validation.html#leave-one-out-loo)
# and [here](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html#sklearn.model_selection.LeaveOneOut)
# for more details.
loo = LeaveOneOut()
# Keep track of successful predictions
successes = []
# the `split` method generates indices to split data into training and test set.
for train_index, test_index in loo.split(X):
# `fit` classifier on training indices
knn.fit(X[train_index], y[train_index])
# `score` classifier on testing indices; since there will be only one
# test index, the score will be either 1 (for a correct prediction) or
# 0 (for an incorrect prediction).
successes.append(knn.score(X[test_index], y[test_index]))
# Divide `successes` by the sample size to get the percentage score.
print("Accuracy for iris dataset with Leave-One-Out validation is {}.\n".
format(np.mean(successes)))
'DiagnosisGroup'
]],
how='inner')
new_df2 = new_df2.fillna(0)
X = new_df2.drop(['DiagnosisGroup'], axis=1)
regex = re.compile(r"\[|\]|<", re.IGNORECASE)
X.columns = [
regex.sub("_", col) if any(x in str(col)
for x in set(('[', ']', '<'))) else col
for col in X.columns.values
]
y = new_df2['DiagnosisGroup']
loo = LeaveOneOut()
y_pred_list = []
auc = []
auc_train = []
for train_index, test_index in loo.split(X):
train_index = list(train_index)
# print("%s %s" % (train_index, test_index))
X_train, X_test = X.iloc[train_index, :], X.iloc[test_index, :]
y_train, y_test = y[train_index], y[test_index]
model = XGBClassifier(
max_depth=4,
n_estimators=150,
learning_rate=15 / 100,
objective='multi:softmax',
reg_lambda=150
#objective='binary:logistic',
output_train = "{}({}: {}) ".format(output_train, i, data[i])
for i in test:
bar[i] = "T"
output_test = "{}({}: {}) ".format(output_test, i, data[i])
print("[ {} ]".format(" ".join(bar)))
print("Train: {}".format(output_train))
print("Test: {}\n".format(output_test))
# Create some data to split with
data = numpy.array([[1, 2], [3, 4], [5, 6], [7, 8]])
# Our two methods
loocv = LeaveOneOut()
lpocv = LeavePOut(p=P_VAL)
split_loocv = loocv.split(data)
split_lpocv = lpocv.split(data)
print("""\
The Leave-P-Out method works by using every combination of P points as test data.
The following output shows the result of splitting some sample data by Leave-One-Out and Leave-P-Out methods.
A bar displaying the current train-test split as well as the actual data points are displayed for each split.
In the bar, "-" is a training point and "T" is a test point.
""")
print("Data:\n{}\n".format(data))
frases = []
f = open("intensoes.txt", "r")
for x in f:
classe, texto = x.split(">>")
intensao.append(classe)
frases.append(texto.rstrip()) #rstrip remove o \n
#Converte as sentenças em BOW
vectorizer = TfidfVectorizer(sublinear_tf=True,
max_df=0.5,
strip_accents='unicode')
intensaoBow = vectorizer.fit_transform(frases)
intensaoNumpy = np.array(intensao)
leaveOneOut = LeaveOneOut()
leaveOneOut.get_n_splits(intensaoBow)
result = []
for train_index, test_index in leaveOneOut.split(intensaoBow):
X_train, X_test = intensaoBow[train_index], intensaoBow[test_index]
y_train, y_test = intensaoNumpy[train_index], intensaoNumpy[test_index]
#KNN
model = KNeighborsClassifier(n_neighbors=1)
model.fit(X_train, y_train)
resultado = model.predict(X_test)[0]
result.append(resultado)
def compare_Estimators_fscore():
from sklearn.svm import LinearSVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import LeaveOneOut
# list of (estimator, param_grid), where param_grid is used in GridSearchCV
classifiers = [
(LinearSVC(random_state=RS, tol=1e-5, C=0.025)),
(KNeighborsClassifier(3)),
(GradientBoostingClassifier(n_estimators=200,
random_state=RS,
learning_rate=0.05)),
GaussianNB(),
]
names = [
'Linear SVC', 'K-Nearest Neighbors', 'Gradient Boosting',
'Gaussian Naive Bayes'
]
columns = 4
fig, axs = plt.subplots(1, columns, figsize=(16, 6))
axs = axs.ravel()
loo = LeaveOneOut()
outputFile = exportDir / '{0}_compare_classifiers'.format(fname)
# iterate over classifiers
for i, clf in enumerate(classifiers):
y_pred = np.zeros(n_samples)
for train_index, test_index in loo.split(X_train):
print("TRAIN:", train_index, "TEST:", test_index)
clf.fit(X_train[train_index, :], y_train[train_index])
y_pred[test_index] = clf.predict(X_train[test_index, :])
precision, recall, fscore, support = precision_recall_fscore_support(
y_train, y_pred)
accuracy = accuracy_score(y_train, y_pred)
index = np.arange(num_categories)
bar_width = 0.3
for c in range(num_categories):
axs[i].bar(index[c],
precision[c],
bar_width,
alpha=1,
color=plt.cm.tab20(i))
axs[i].bar(index[c] + bar_width,
recall[c],
bar_width,
alpha=0.6,
color=plt.cm.tab20(i))
##axs[i].bar( index[c]+bar_width+bar_width, fscore[c], bar_width, alpha=0.25, color=plt.cm.tab20(i), hatch="//", edgecolor=plt.cm.tab20(i) )
axs[i].set_xticks(np.arange(num_categories) + bar_width)
axs[i].set_xticklabels(categories, rotation=45, ha='right')
axs[i].set_xlabel('')
axs[i].set_ylabel('Score')
axs[i].set_title(names[i] + r"$\bf{" + ' | Accuracy: ' +
str(round(accuracy, 2)) + "}$")
plt.tight_layout()
from matplotlib.patches import Patch
legend_elements = [
Patch(facecolor=fc, label='Precision'),
Patch(facecolor=fc, alpha=0.6, label='Recall'),
]
#Patch(facecolor=fc, alpha=0.25, label='F-score', hatch="//") ]
plt.legend(handles=legend_elements, loc='lower right')
df = pd.DataFrame([precision, recall],
columns=categories,
index=['Precision', 'Recall']).transpose()
df.to_csv(Path(str(outputFile) + names[i] + ".csv"),
index=True,
header=True,
sep=',')
plt.savefig(Path(str(outputFile) + ".png"), dpi=dpi_all)
# np.savetxt(Path(str(outputFile)+ ".csv"), prob_loo, delimiter=",", header=",".join(categories ))
# df.to_csv(Path(str(outputFile)+ ".csv"), index=True, sep=',')
if exportPDF:
plt.savefig(Path(str(outputFile) + ".pdf"), dpi=dpi_all)
def perform_plot_LOO():
#######
### Leave-one-samepl-out cross-validation model
#####
n_samples, n_features = X_train.shape
y_pred = np.zeros(n_samples)
class_probs = np.zeros(
[n_samples, np.unique(y_train).size]
) # the probability of assigning each left out sample to each of the classes
loo = LeaveOneOut()
for train_index, test_index in loo.split(X_train):
print("TRAIN:", train_index, "TEST:", test_index)
clf_main.fit(X_train[train_index, :], y_train[train_index])
y_pred[test_index] = clf_main.predict(X_train[test_index, :])
try:
class_probs[test_index, :] = clf_main.predict_proba(
X_train[test_index, :])
except Exception:
pass
# my_score = np.mean(y_pred==y_input)
precision, recall, fscore, support = precision_recall_fscore_support(
y_train, y_pred)
accuracy = accuracy_score(y_train, y_pred)
## MAKE CLASS PROBABILITY PLOT
plt.figure()
arr1inds = y_train.argsort()
labels_train_temp = labels_train.reset_index(drop=True)
labels_train_sorted = labels_train_temp[arr1inds[::-1]]
prob_loo = class_probs[arr1inds[::-1]]
plt.imshow(prob_loo,
cmap=plt.cm.coolwarm,
interpolation='none',
aspect='auto')
plt.grid(True)
plt.yticks(np.arange(n_samples),
labels_train_sorted[0:n_samples],
fontsize=2,
rotation=0)
plt.xticks(np.arange(num_categories),
categories,
fontsize=8,
rotation=45,
ha='right')
ax = plt.gca()
ax.grid(color='w', linestyle='-', linewidth=0)
plt.colorbar()
plt.tight_layout()
outputFile = exportDir / '{0}_class_probs_leave_one_out'.format(fname)
plt.savefig(Path(str(outputFile) + ".png"), dpi=dpi_all)
# np.savetxt(Path(str(outputFile)+ ".csv"), prob_loo, delimiter=",", header=",".join(categories ))
df = pd.DataFrame(prob_loo,
index=labels_train[0:n_samples],
columns=categories)
df.to_csv(Path(str(outputFile) + ".csv"), index=True, header=True, sep=',')
if exportPDF:
plt.savefig(Path(str(outputFile) + ".pdf"), dpi=dpi_all)
## PRECISION RECALL PLOT
plotPrecisionRecall(precision, recall, categories, accuracy)
outputFile = exportDir / '{0}_precision_recall_training'.format(fname)
plt.savefig(Path(str(outputFile) + ".png"), dpi=dpi_all)
data = {'Precision': precision, 'Recall': recall}
df = pd.DataFrame(data, index=categories)
df.to_csv(Path(str(outputFile) + ".csv"), index=True, header=True, sep=',')
if exportPDF:
plt.savefig(Path(str(outputFile) + ".pdf"), dpi=dpi_all)
def _print_train_results(classifier_name, classifier, regressors, response, regressor_names, leave_one_out):
"""
_print_train_results
Performs validation tests of the model and prints the results
:param classifier_name: Name of the classifier method
:param classifier: Classifier object
:param regressors: numpy array with the regressors used to train the model
:param response: numpy array with the response used to train the model
:param regressor_names: List with the name of the regressors
:param leave_one_out: Boolean, true to perform leave-one-out cross-validation, otherwise perform default cross
validation
:return: None
"""
global MESSAGES
_verbose_print("classifier_name: {}".format(classifier_name))
_verbose_print("classifier: {}".format(classifier))
_verbose_print("regressor_names: {}".format(regressor_names))
_verbose_print("leave_one_out: {}".format(leave_one_out))
MESSAGES.AddMessage("{} classifier with parameters: \n {}".format(classifier_name,
str(classifier.get_params()).replace("'", "")))
if leave_one_out:
# create a leave-one-out instance to execute the cross-validation
loo = LeaveOneOut()
start = timer()
cv_score = cross_val_score(classifier, regressors, response, cv=loo.split(regressors))
end = timer()
n_tests = len(response)
MESSAGES.AddMessage("Score (Leave one Out):" + str(cv_score.mean()))
else:
start = timer()
cv_score = cross_val_score(classifier, regressors, response)
end = timer()
n_tests = 3
MESSAGES.AddMessage("Score (3-Fold):" + str(cv_score.mean()))
# Print validation time
MESSAGES.AddMessage("Testing time: {:.3f} seconds, {:.3f} seconds per test".format(end - start,
(end - start) / n_tests))
# Print confusion matrix
MESSAGES.AddMessage("Confusion Matrix (Train Set):")
confusion = confusion_matrix(response, classifier.predict(regressors))
labels = ["Non Deposit", "Deposit"]
row_format = "{:6}" + "{:^16}" * (len(labels) + 1)
MESSAGES.AddMessage(row_format.format("", "", "Predicted", ""))
MESSAGES.AddMessage(row_format.format("True", "", *labels))
for label, row in zip(labels, confusion):
MESSAGES.AddMessage(row_format.format("", label, *row))
# Some classifiers do not have decision_function attribute but count with predict_proba instead
# TODO: Generalize to anything that does not have decision_function "Easier to ask for forgiveness than permission"
if classifier_name in ["Random Forest"]:
des_fun = classifier.predict_proba(regressors)[:, classifier.classes_ == 1]
else:
des_fun = classifier.decision_function(regressors)
MESSAGES.AddMessage("Area Under the curve (AUC): {}".format(roc_auc_score(response, des_fun)))
# Give the importance of the features if it is supported
# TODO: Generalize to anything that does have feature_importances_ "Easier to ask for forgiveness than permission"
if classifier_name == "Adaboost":
MESSAGES.AddMessage("Feature importances: ")
importances = [[name, val*100] for name, val in zip(regressor_names, classifier.feature_importances_)]
long_word = max([len(x) for x in regressor_names])
row_format = "{" + ":" + str(long_word) + "} {:4.1f}%"
# Print regressors in descending importance, omit the ones with 0 importance
for elem in sorted(importances, key=lambda imp: imp[1], reverse=True):
if elem[1] > 0:
MESSAGES.AddMessage(row_format.format(*elem))
return
'''
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import cross_val_predict
# log-regression lib model
log_model = LogisticRegression()
m = np.shape(X)[0]
# 10-folds CV
y_pred = cross_val_predict(log_model, X, y, cv=10)
print(metrics.accuracy_score(y, y_pred))
# LOOCV
from sklearn.model_selection import LeaveOneOut
loo = LeaveOneOut()
accuracy = 0;
for train, test in loo.split(X):
log_model.fit(X[train], y[train]) # fitting
y_p = log_model.predict(X[test])
if y_p == y[test] : accuracy += 1
print(accuracy / np.shape(X)[0])
# m = np.shape(X)[0]
# scores_loo = cross_val_score(log_model, X, y, cv=m)
# print(scores_loo)
# # prediction using 10-folds
# y_pred_loo = cross_val_predict(log_model, X, y, cv=m)
# print(metrics.accuracy_score(y, y_pred_loo))
'''
