def run(self):
print "Preparing the environment"
self.prepareEnvironment()
print "Reading in the training data"
imageCollections = data_io.get_train_df()
wndchrmWorker = WndchrmWorkerTrain()
print "Getting features"
if not self.loadWndchrm: #Last wndchrm set of features
featureGetter = FeatureGetter()
fileName = data_io.get_savez_name()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates, train) = Utils.calculateFeatures(fileName, featureGetter, imageCollections)
else:
(namesObservations, coordinates, train) = Utils.loadFeatures(fileName)
print "Getting target vector"
(indexes, target, obs) = featureGetter.getTargetVector(coordinates, namesObservations, train)
print "Saving images"
imageSaver = ImageSaver(coordinates[indexes], namesObservations[indexes],
imageCollections, featureGetter.patchSize, target[indexes])
imageSaver.saveImages()
print "Executing wndchrm algorithm and extracting features"
(train, target) = wndchrmWorker.executeWndchrm()
else:
(train, target) = wndchrmWorker.loadWndchrmFeatures()
print "Training the model"
model = RandomForestClassifier(n_estimators=500, verbose=2, n_jobs=1, min_samples_split=30, random_state=1, compute_importances=True)
model.fit(train, target)
print model.feature_importances_
print "Saving the classifier"
data_io.save_model(model)
def main():
classifier1 = RandomForestClassifier(n_estimators = 100, max_features=0.5, max_depth=5.0)
classifier2 = DecisionTreeClassifier(max_depth = 10, criterion = 'entropy', random_state = 0)
classifier3 = KNeighborsClassifier(n_neighbors = 5, p = 2, metric = 'minkowski')
classifier4 = SVC(kernel = 'rbf', C = 10.0, random_state = 0, gamma = 0.10)
classifier5 = LogisticRegression(penalty = 'l2', C = 1.0, random_state = 0)
classifier6 = GaussianNB()
print("Reading in the training data")
train = data_io.get_train_df()
print ("Cleaning data. Check here for imputation, One hot encoding and factorization procedures..")
train = FeatureConverter().clean_data(train)
train.drop(['PassengerId'], axis = 1, inplace = True)
#print train.head()
train = train.values
eclf = EnsembleClassifier(clfs = [classifier1, classifier2, classifier3, classifier5, classifier6], voting = 'hard')
#eclf = EnsembleClassifier(clfs = [classifier2], voting = 'hard')
scores = cross_val_score(estimator = eclf, X = train[0:,1:], y = train[0:,0], cv = 10, scoring = 'roc_auc')
print("Accuracy: %0.4f (+/- %0.3f)" % (scores.mean(), scores.std()))
eclf.fit(train[0:,1:],train[0:,0])
print("Saving the classifier")
data_io.save_model(eclf)
def runWithoutWndchrm(self):
print "Reading in the training data"
imageCollections = data_io.get_train_df()
print "Getting features"
featureGetter = FeatureGetter()
fileName = data_io.get_savez_name()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates,
train) = Utils.calculateFeatures(fileName, featureGetter,
imageCollections)
else:
(namesObservations, coordinates,
train) = Utils.loadFeatures(fileName)
print "Getting target vector"
(indexes, target,
obs) = featureGetter.getTargetVector(coordinates, namesObservations,
train)
print "Training the model"
classifier = RandomForestClassifier(n_estimators=500,
verbose=2,
n_jobs=1,
min_samples_split=10,
random_state=1,
compute_importances=True)
#classifier = KNeighborsClassifier(n_neighbors=50)
model = Pipeline([('scaling', MinMaxScaler()),
('classifying', classifier)])
model.fit(obs[indexes], target[indexes])
print "Saving the classifier"
data_io.save_model(model)
def main():
classifier1 = RandomForestClassifier(n_estimators = 100, max_features=0.5, max_depth=5.0)
classifier2 = DecisionTreeClassifier(max_depth = 10, criterion = 'entropy', random_state = 0)
classifier3 = KNeighborsClassifier(n_neighbors = 5, p = 2, metric = 'minkowski')
classifier4 = SVC(kernel = 'rbf', C = 10.0, random_state = 0, gamma = 0.10)
classifier5 = LogisticRegression(penalty = 'l2', C = 1.0, random_state = 0)
classifier6 = GaussianNB()
classifier7 = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)
print("Reading in the training data")
train = data_io.get_train_df()
print ("Cleaning data. Check here for imputation, One hot encoding and factorization procedures..")
train = FeatureConverter().clean_data(train)
train.drop(['Id'], axis = 1, inplace = True)
#print train.head()
train = train.values
#eclf = EnsembleClassifier(clfs = [classifier1, classifier2, classifier3, classifier5, classifier6], voting = 'hard')
#eclf = EnsembleClassifier(clfs = [classifier1], voting = 'hard')
eclf = classifier3
#scores = cross_val_score(estimator = eclf, X = train[0:,0:-1], y = train[0:,-1], cv = 10, scoring = 'roc_auc')
#print("Accuracy: %0.4f (+/- %0.3f)" % (scores.mean(), scores.std()))
eclf.fit(train[0:,0:-1],train[0:,-1])
# importances = eclf.feature_importances_
# indices = np.argsort(importances)[::-1]
# for f in range(train[0:,0:-1].shape[1]):
# print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
#
print("Saving the classifier")
data_io.save_model(eclf)
def get_cv_score():
classifier = data_io.load_model()
train = data_io.get_train_df()
scores = cv.cross_val_score(classifier, train[[x for x in train.columns if x != 'label']], train['label'])
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
def main():
print("Reading in the training data")
data = data_io.get_train_df()
print("Extracting features")
feature_extractor = Vectorizer(MAX_FEATURES)
category_vectorizer = DictVectorizer()
#category_title = pd.get_dummies(train['Title'])
#print (category_vectorizer.shape, X.shape)
X = form_input(data, feature_extractor, category_vectorizer)
#location = pd.get_dummies(train['LocationNormalized'])
#X = hstack((X, location))
#contract_time = pd.get_dummies(train['ContractTime'])
#X = hstack((X, contract_time))
#print(X)
y = data["SalaryNormalized"]
print("Training model")
linreg.train(X, y)
print("Making predictions")
predictions = linreg.predict(X)
mae_train = metrics.MAE(predictions, data["SalaryNormalized"])
print('MAE train=%s', mae_train)
print("Validating...")
data = data_io.get_valid_df()
X = form_input(data, feature_extractor, category_vectorizer, train=False)
predictions = linreg.predict(X)
data_io.write_submission(predictions)
'''
def get_cv_score():
classifier = data_io.load_model()
train = data_io.get_train_df()
scores = cv.cross_val_score(
classifier, train[[x for x in train.columns if x != 'label']],
train['label'])
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
def main():
print("Reading in the training data")
train = data_io.get_train_df()
print("Extracting features and training model")
for key in train:
classifier = get_pipeline(train[key])
classifier.fit(train[key], train[key]["SalaryNormalized"])
print("Saving the classifier for %s" %key)
data_io.save_model(classifier,key)
def main():
print("Reading in the training data")
train = data_io.get_train_df()
print("Extracting features and training model")
classifier = get_pipeline()
classifier.fit(train, train["SalaryNormalized"])
print("Saving the classifier")
data_io.save_model(classifier)
def main():
print("Reading in the training data")
train = data_io.get_train_df()
print("Extracting features and training model")
classifier = get_pipeline()
classifier.fit(train[[x for x in train.columns if x != 'label']], train['label'])
print("Saving the classifier")
data_io.save_model(classifier)
def main():
print("Reading in the training data")
train = data_io.get_train_df()
print("Extracting features and training model")
classifier = get_pipeline(train)
classifier.fit(train, train["SalaryNormalized"])
print("Saving the classifier")
data_io.save_model(classifier)
def main():
print("Reading in the training data")
train = data_io.get_train_df()
print("Extracting features and training model")
classifier = get_pipeline()
classifier.fit(train[[x for x in train.columns if x != 'label']],
train['label'])
print("Saving the classifier")
data_io.save_model(classifier)
def main():
print("Reading in the training data")
train = data_io.get_train_df()
mean = train["SalaryNormalized"].mean()
print("The mean salary is %f" % mean)
print("Saving the model")
data_io.save_model(mean)
predictions = [mean] * len(train)
print(metrics.MAE(predictions, train["SalaryNormalized"].tolist()))
def main():
print("Reading in the training data")
train = data_io.get_train_df()
mean = train["SalaryNormalized"].mean()
print("The mean salary is %f" % mean)
print("Saving the model")
data_io.save_model(mean)
predictions = [mean] * len(train)
print(metrics.MAE(predictions, train["SalaryNormalized"].tolist()))
def main():
markdown = PagedownToHtml()
print("Reading in the training data")
train = data_io.get_train_df()
for i in train.index:
train["BodyMarkdown"][i] = markdown.convert(train["BodyMarkdown"][i])
print("Extracting features and training")
classifier = get_pipeline()
classifier.fit(train, train["OpenStatus"])
print("Saving the classifier")
data_io.save_model(classifier, "model.pickle")
model = data_io.load_model("model.pickle")
def runWithoutWndchrm(self):
print "Reading in the training data"
imageCollections = data_io.get_train_df()
print "Getting features"
featureGetter = FeatureGetter()
fileName = data_io.get_savez_name()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates, train) = Utils.calculateFeatures(fileName, featureGetter, imageCollections)
else:
(namesObservations, coordinates, train) = Utils.loadFeatures(fileName)
print "Getting target vector"
(indexes, target, obs) = featureGetter.getTargetVector(coordinates, namesObservations, train)
print "Training the model"
classifier = RandomForestClassifier(n_estimators=500, verbose=2, n_jobs=1, min_samples_split=10, random_state=1, compute_importances=True)
#classifier = KNeighborsClassifier(n_neighbors=50)
model = Pipeline([('scaling', MinMaxScaler()), ('classifying', classifier)])
model.fit(obs[indexes], target[indexes])
print "Saving the classifier"
data_io.save_model(model)
def run(self):
print "Preparing the environment"
self.prepareEnvironment()
print "Reading in the training data"
imageCollections = data_io.get_train_df()
wndchrmWorker = WndchrmWorkerTrain()
print "Getting features"
if not self.loadWndchrm: #Last wndchrm set of features
featureGetter = FeatureGetter()
fileName = data_io.get_savez_name()
if not self.load: #Last features calculated from candidates
(namesObservations, coordinates,
train) = Utils.calculateFeatures(fileName, featureGetter,
imageCollections)
else:
(namesObservations, coordinates,
train) = Utils.loadFeatures(fileName)
print "Getting target vector"
(indexes, target,
obs) = featureGetter.getTargetVector(coordinates,
namesObservations, train)
print "Saving images"
imageSaver = ImageSaver(coordinates[indexes],
namesObservations[indexes],
imageCollections, featureGetter.patchSize,
target[indexes])
imageSaver.saveImages()
print "Executing wndchrm algorithm and extracting features"
(train, target) = wndchrmWorker.executeWndchrm()
else:
(train, target) = wndchrmWorker.loadWndchrmFeatures()
print "Training the model"
model = RandomForestClassifier(n_estimators=500,
verbose=2,
n_jobs=1,
min_samples_split=30,
random_state=1,
compute_importances=True)
model.fit(train, target)
print model.feature_importances_
print "Saving the classifier"
data_io.save_model(model)
def checkCandidates(self):
imageCollections = data_io.get_train_df()
featureGetter = FeatureGetter()
(namesObservations, coordinates,
train) = featureGetter.getTransformedDatasetChecking(imageCollections)
imageNames = namesObservations
currentImage = imageNames[0]
csvArray = Utils.readcsv(imageNames[0])
mitoticPointsDetected = 0
totalMitoticPoints = len(csvArray)
finalTrain = []
for i in range(len(coordinates)):
if imageNames[i] != currentImage:
csvArray = Utils.readcsv(imageNames[i])
totalMitoticPoints += len(csvArray)
currentImage = imageNames[i]
for point in csvArray:
if ((point[0] - coordinates[i][0])**2 +
(point[1] - coordinates[i][1])**2) < 30**2:
mitoticPointsDetected += 1
csvArray.remove(point)
finalTrain.append(train[i])
break
finalTrain = np.array(finalTrain)
allArea = finalTrain[:, 0]
allPerimeter = finalTrain[:, 1]
allRoundness = finalTrain[:, 2]
totalObservations = len(coordinates)
print "Minimum Area: %f" % np.min(allArea)
print "Minimum Perimeter: %f" % np.min(allPerimeter)
print "Minimum Roundness: %f" % np.min(allRoundness)
print "Maximum Area: %f" % np.max(allArea)
print "Maximum Perimeter: %f" % np.max(allPerimeter)
print "Maximum Roundness: %f" % np.max(allRoundness)
print "Total number of candidates: %d" % (totalObservations)
print "Total number of mitotic points: %d" % (totalMitoticPoints)
print "Mitotic points detected: %d" % (mitoticPointsDetected)
print "Mitotic points missed: %d" % (totalMitoticPoints -
mitoticPointsDetected)
def checkCandidates(self):
imageCollections = data_io.get_train_df()
featureGetter = FeatureGetter()
(namesObservations, coordinates, train) = featureGetter.getTransformedDatasetChecking(imageCollections)
imageNames = namesObservations
currentImage = imageNames[0]
csvArray = Utils.readcsv(imageNames[0])
mitoticPointsDetected = 0
totalMitoticPoints = len(csvArray)
finalTrain = []
for i in range(len(coordinates)):
if imageNames[i] != currentImage:
csvArray = Utils.readcsv(imageNames[i])
totalMitoticPoints += len(csvArray)
currentImage = imageNames[i]
for point in csvArray:
if ((point[0]-coordinates[i][0]) ** 2 + (point[1]-coordinates[i][1]) ** 2)< 30**2:
mitoticPointsDetected += 1
csvArray.remove(point)
finalTrain.append(train[i])
break
finalTrain = np.array(finalTrain)
allArea = finalTrain[:,0]
allPerimeter = finalTrain[:,1]
allRoundness = finalTrain[:,2]
totalObservations = len(coordinates)
print "Minimum Area: %f" % np.min(allArea)
print "Minimum Perimeter: %f" % np.min(allPerimeter)
print "Minimum Roundness: %f" % np.min(allRoundness)
print "Maximum Area: %f" % np.max(allArea)
print "Maximum Perimeter: %f" % np.max(allPerimeter)
print "Maximum Roundness: %f" % np.max(allRoundness)
print "Total number of candidates: %d" % (totalObservations)
print "Total number of mitotic points: %d" %(totalMitoticPoints)
print "Mitotic points detected: %d" %(mitoticPointsDetected)
print "Mitotic points missed: %d" %(totalMitoticPoints-mitoticPointsDetected)
{
'C': [0.001, 0.1, 1.0, 10.0, 100.0],
'penalty': ['l1', 'l2']
}
svc_params_grid = \
{
'C': [0.001, 0.1, 1.0, 10.0, 100.0],
'kernel': ['linear', 'rbf'],
'gamma': [0.001, 0.1, 1.0, 10.0, 100.0]
}
if __name__ == "__main__":
print("Reading in the training data")
train = data_io.get_train_df()
print(
"Cleaning data. Check here for imputation, One hot encoding and factorization procedures.."
)
train = FeatureConverter().clean_data(train)
train.drop(['PassengerId'], axis=1, inplace=True)
#print train.head()
train = train.values
grid_search = GridSearchCV(RandomForestClassifier(n_estimators=100),
rf_params_grid,
cv=5,
verbose=1)
grid_search.fit(train[0:, 1:], train[0:, 0])
{
'C': [0.001, 0.1, 1.0, 10.0, 100.0],
'penalty': ['l1', 'l2']
}
svc_params_grid = \
{
'C': [0.001, 0.1, 1.0, 10.0, 100.0],
'kernel': ['linear', 'rbf'],
'gamma': [0.001, 0.1, 1.0, 10.0, 100.0]
}
if __name__=="__main__":
print("Reading in the training data")
train = data_io.get_train_df()
print ("Cleaning data. Check here for imputation, One hot encoding and factorization procedures..")
train = FeatureConverter().clean_data(train)
train.drop(['PassengerId'], axis = 1, inplace = True)
#print train.head()
train = train.values
grid_search = GridSearchCV(RandomForestClassifier(n_estimators = 100), rf_params_grid, cv = 5, verbose = 1)
grid_search.fit(train[0:,1:],train[0:,0])
print grid_search.best_params_
grid_search = GridSearchCV(LogisticRegression(random_state = 0), lr_params_grid, cv = 5, verbose = 1)
grid_search.fit(train[0:,1:],train[0:,0])
