def grid_search():
comments, labels = load_data()
param_grid = dict(logr__C=np.arange(1, 20, 5))
clf = build_nltk_model()
cv = ShuffleSplit(len(comments), n_iterations=10, test_size=0.2)
grid = GridSearchCV(clf,
cv=cv,
param_grid=param_grid,
verbose=4,
n_jobs=12,
score_func=auc_score)
grid.fit(comments, labels)
print(grid.best_score_)
print(grid.best_params_)
tracer()
cv_scores = grid.scores_
for param in cv_scores.params:
means, errors = cv_scores.accumulated(param, 'max')
plt.errorbar(cv_scores.values[param], means, yerr=errors)
plt.xlabel(param)
plt.ylim((0.85, 0.93))
plt.savefig("grid_plot_%s.png" % param)
plt.close()
comments_test, dates_test = load_test()
prob_pred = grid.best_estimator_.predict_proba(comments_test)
write_test(prob_pred[:, 1])
def loadData():
global dataChoice
if dataChoice == 'sim':
train_filename = 'r-train100.csv'
test_filename = 'r-test100.csv'
user_filename = 'u100.csv'
else:
# dataChoice == 'validate' or dataChoice == 'full'
train_filename = 'ratings-train.csv'
test_filename = 'ratings-test.csv'
user_filename = 'users.csv'
training_data = util.load_train(train_filename)
test_queries = util.load_test(test_filename)
user_list = util.load_users(user_filename)
validation_set = {}
if dataChoice != 'full':
# split training_data into 80% training and 20% validation
split = int(len(training_data) * 0.8)
validation_set = training_data[split:]
training_data = training_data[:split]
return training_data, test_queries, user_list, validation_set
def loadData():
global dataChoice
if dataChoice == 'sim':
train_filename = 'r-train100.csv'
test_filename = 'r-test100.csv'
user_filename = 'u100.csv'
else:
# dataChoice == 'validate' or dataChoice == 'full'
train_filename = 'ratings-train.csv'
test_filename = 'ratings-test.csv'
user_filename = 'users.csv'
training_data = util.load_train(train_filename)
test_queries = util.load_test(test_filename)
user_list = util.load_users(user_filename)
validation_set = {}
if dataChoice != 'full':
# split training_data into 80% training and 20% validation
split = int(len(training_data) * 0.8)
validation_set = training_data[split:]
training_data = training_data[:split]
return training_data, test_queries, user_list, validation_set
def make_predictions(ratings_data, mfact_data):
"""
Makes a set of predictions, suitable for passing to util.write_predictions
Arguments:
ratings_data : the data object returned by build_ratings
mfact_data : the data object returned by mfact
Returns:
predictions : a list of dicts, suitable for passing to util.write_predictions
"""
# load data from the original training set
center = ratings_data["center"]
scale = ratings_data["scale"]
book_isbn_to_index = ratings_data["book_isbn_to_index"]
# load data calculated by the matrix factorization
P = mfact_data["P"]
Q = mfact_data["Q"]
Bn = mfact_data["Bn"]
Bd = mfact_data["Bd"]
mean = mfact_data["mean"]
# load the set of requested predictions
queries = util.load_test("../data/books/ratings-test.csv")
L = len(queries)
debug("Making %d predictions",L)
# for each query
for (i,query) in enumerate(queries):
# print progress
# if DEBUG: print ("%d / %d : " % (i+1,L)),
# lookup user and book index
user_index = query["user"] - 1
book_index = book_isbn_to_index[query["isbn"]]
# calculate predicted rating
#rating_float = (np.dot(P[user_index,:],Q[book_index,:]) + mean + Bn[user_index] + Bd[book_index]) \
# * scale + center
rating_float = np.dot(P[user_index,:],Q[book_index,:]) * scale + center
# coerce to range (1,5); round
rating = max(1,min(5,rating_float))
# store both values so we can do visualization of distributions later
query["rating"] = rating
query["rating_f"] = rating_float
# print value
# if DEBUG: print "%f -> %d" % (rating_float, rating)
return queries
def test_data():
path = 'my_model.h5'
model = keras.models.load_model(path)
_, _, _, _, tokenizer = util.load_train_val()
X, y = util.load_test(path_data, tokenizer)
res = model.eval(x=X, y=y, batch_size=200)
print(res)
def apply_models():
comments, labels = load_extended_data()
comments_test = load_test("impermium_verification_set_.csv")
clf1 = build_base_model()
clf2 = build_elasticnet_model()
clf3 = build_stacked_model()
clf4 = build_nltk_model()
models = [clf1, clf2, clf3, clf4]
probs_common = np.zeros((len(comments_test), 2))
for i, clf in enumerate(models):
clf.fit(comments, labels)
probs = clf.predict_proba(comments_test)
#print("score: %f" % auc_score(labels_test, probs[:, 1]))
probs_common += probs
write_test(probs[:, 1], "test_prediction_model_%d.csv" % i,
ds="impermium_verification_set_.csv")
probs_common /= 4.
#score = auc_score(labels_test, probs_common[:, 1])
#print("combined score: %f" % score)
write_test(probs_common[:, 1], "test_prediction_combined.csv",
ds="impermium_verification_set_.csv")
def apply_models():
comments, labels = load_extended_data()
comments_test = load_test("impermium_verification_set_.csv")
clf1 = build_base_model()
clf2 = build_elasticnet_model()
clf3 = build_stacked_model()
clf4 = build_nltk_model()
models = [clf1, clf2, clf3, clf4]
probs_common = np.zeros((len(comments_test), 2))
for i, clf in enumerate(models):
clf.fit(comments, labels)
probs = clf.predict_proba(comments_test)
#print("score: %f" % auc_score(labels_test, probs[:, 1]))
probs_common += probs
write_test(probs[:, 1],
"test_prediction_model_%d.csv" % i,
ds="impermium_verification_set_.csv")
probs_common /= 4.
#score = auc_score(labels_test, probs_common[:, 1])
#print("combined score: %f" % score)
write_test(probs_common[:, 1],
"test_prediction_combined.csv",
ds="impermium_verification_set_.csv")
def grid_search():
comments, labels = load_data()
param_grid = dict(logr__C=np.arange(1, 20, 5))
clf = build_nltk_model()
cv = ShuffleSplit(len(comments), n_iterations=10, test_size=0.2)
grid = GridSearchCV(clf, cv=cv, param_grid=param_grid, verbose=4,
n_jobs=12, score_func=auc_score)
grid.fit(comments, labels)
print(grid.best_score_)
print(grid.best_params_)
tracer()
cv_scores = grid.scores_
for param in cv_scores.params:
means, errors = cv_scores.accumulated(param, 'max')
plt.errorbar(cv_scores.values[param], means, yerr=errors)
plt.xlabel(param)
plt.ylim((0.85, 0.93))
plt.savefig("grid_plot_%s.png" % param)
plt.close()
comments_test, dates_test = load_test()
prob_pred = grid.best_estimator_.predict_proba(comments_test)
write_test(prob_pred[:, 1])
# acc = util.model_roc_score(learner, test_dat)
acc = util.model_score(learner, test_dat)
test_acc.append(acc)
learned_size.append(total_pool_size - prof.get_pool_size() + init_size)
if ITER_ENABLE:
if count < 0: break
count -= 1
return test_acc, learned_size
pool_dat = load_pool()
init_dat = load_init()
test_dat = load_test()
train_pool = np.array(gen_land_pool(pool_dat))
shuffle(train_pool)
print "[info]Start passive learning..."
test_acc_ps, learned_size_ps = run_stl_landm(pool_dat,
init_dat,
test_dat,
do_active=False)
util.curve_to_csv("res/ps_stl_non.csv", test_acc_ps, learned_size_ps)
print "[info]Start active learning..."
test_acc_ac, learned_size_ac = run_stl_landm(pool_dat,
init_dat,
test_dat,
# coding=utf8 import numpy as np import pandas as pd from scipy import interp from matplotlib import pyplot as plt from sklearn import preprocessing from sklearn.metrics import roc_curve, auc # load data # X1 = pd.read_csv(r'Data/Train/PPD_Training_Master_GBK_3_1_Training_Set.csv', encoding='gbk') X2 = pd.read_csv(r'Data/Test/PPD_Master_GBK_2_Test_Set.csv')# , encoding='gbk') from util import load_train, load_test X_train, y_train, w_train = load_train() X_test = load_test() print 'data loaded, transforming...' ''' 3.19 commented scaler = preprocessing.StandardScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) # train and predict from sklearn.linear_model import LogisticRegression clf = LogisticRegression(n_jobs=-1, class_weight='balanced', penalty='l1') clf.fit(X_train, y_train) probas_ = clf.predict_proba(X_test) # visualization on training set fpr, tpr, thresholds = roc_curve(y_train, p2[:, 1]) mean_tpr += interp(mean_fpr, fpr, tpr) mean_tpr[0] = 0.0 roc_auc = auc(fpr, tpr)
# # do prediction based on matrix factorization
# K = 30
# run = 0
# step = 280
# mfact = su.unpickle("output/mfact_%d_run_%d/mfact_%d_%d" % (K, run, K, step))
# P = mfact["P"]
# Q = mfact["Q"]
# Bn = mfact["Bn"]
# Bd = mfact["Bd"]
# # this is the mean of the standardized training data, used for the learning/
# # prediction
# standard_mean = mfact["mean"]
# load the set of requested predictions
queries = util.load_test("../data/books/ratings-test.csv")
L = len(queries)
# for each query
for (i,query) in enumerate(queries):
print ("%d / %d : " % (i+1,L)),
user_index = query["user"] - 1
book_index = book_isbn_to_index[query["isbn"]]
# calculate predicted rating
rating_float = (np.dot(P[user_index,:],Q[book_index,:]) + standard_mean + Bn[user_index] + Bd[book_index]) * std + mean
# coerce to range (1,5); round, convert to int
rating = int(round(max(1,min(5,rating_float))))
# store both values so we can do visualization of distributions later
def generateFullTestMatrix(detectorSettings=(1, 1, 1), partitions=(9, 9, 9)):
testMatrix = []
for i in range(0, util.TEST_COUNT - 1):
testMatrix.append(generateEdgeFeaturesVector(util.load_test(i)))
return testMatrix
def predict(train, test, pred_file):
y_hat, train_rss = run_model(train, test, 'prediction', 0)
for i, yi in enumerate(y_hat):
if yi < 0:
y_hat[i] = 0
if yi > 5:
y_hat[i] = 5
for i, entry in enumerate(test):
entry['rating'] = float(y_hat[i])
util.write_predictions(test, pred_file)
# prediction mode
test_filename = 'data/ratings-test.csv'
test = util.load_test(test_filename)
pred_filename = 'predictions/sgd_converged.csv'
predict(train_valid, test, pred_filename)
"""
x = np.zeros((n, r, 2)) # 2 layers, 1 for train predictions and 1 for valid predictions
y = np.zeros((n, 1))
for i, entry in enumerate(train_valid):
y[i] = float(entry['rating'])
def build_matrix(m, v, fold_idx, param_idx):
span = np.shape(v)[0]
# train predictions
import numpy as np
import util
# This makes predictions based on the mean rating for each user in the
# training data. When there are no training data for a user, it
# defaults to the global mean.
pred_filename = 'pred-user-mean.csv'
train_filename = 'ratings-train.csv'
test_filename = 'ratings-test.csv'
user_filename = 'users.csv'
training_data = util.load_train(train_filename)
test_queries = util.load_test(test_filename)
user_list = util.load_users(user_filename)
# Compute the global mean rating for a fallback.
num_train = len(training_data)
mean_rating = float(sum(map(lambda x: x['rating'], training_data))) / num_train
print "The global mean rating is %0.3f." % (mean_rating)
# Turn the list of users into a dictionary.
# Store data for each user to keep track of the per-user average.
users = {}
for user in user_list:
users[user['user']] = {
'total': 0, # For storing the total of ratings.
'count': 0, # For storing the number of ratings.
}
# Iterate over the training data to compute means.
import time
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from util import load_training, load_test, evaluate, standard
import warnings
warnings.filterwarnings('ignore')
### 此处定义参数
n_components = 100 # PCA降至的维数
C = 1 # 软间隔系数
decision_function = 'ovr' # 'ovo' for OneVsOne and 'ovr' for OneVsRest'
kernel = 'rbf' # 核函数类型 'rbf', 'linear', 'poly' or 'sigmoid'
gamma = 1e-5 # 针对rbf, gamma越大，支持向量越少
#####
training_data, training_label = load_training()
test_data, test_label = load_test()
print('training size: {}'.format(len(training_label)))
print('test size: {}'.format(len(test_label)))
# 展成一维
training_data = np.array([x.flatten() for x in training_data])
training_label = np.array(training_label)
test_data = np.array([x.flatten() for x in test_data])
test_label = np.array(test_label)
pca = PCA(n_components=n_components)
model = SVC(C=C,
random_state=0,
max_iter=1000,
kernel=kernel,
import numpy as np
import ensemble
from train_model_library import train_model_library
import util
import makePred
ensemble_library_pred, validation_labels, scaler, model_grid = train_model_library(
n_folds_to_compute=1)
ensemble, acc, n, c1acc = ensemble.generate_ensemble(ensemble_library_pred,
validation_labels,
n_init=3,
tolerance=.00001)
ids, features = util.load_test("kaggle_test_tf_idf_l1_norm.csv")
labels = makePred.makePrediction(ensemble, model_grid, features, scaler)
util.write_predictions(labels, "idflabels_lean_2.csv")
print("done")
def reload_dat():
gc.collect()
pool_dat = load_pool()
init_dat = load_init()
test_dat = load_test()
return pool_dat, init_dat, test_dat
ELLA_DIR = "/home/stpanda/Dropbox/STDreamSoft/Academics/SeniorThesis/Projects/al_ella/lib/ELLAv1.0"
eng.addpath("/home/stpanda/Dropbox/STDreamSoft/Academics/SeniorThesis/Projects/al_ella/ml")
eng.addpath(eng.genpath(ELLA_DIR))
# res = eng.runExperimentActiveTask()
# print res
######## Panda ######
# Comparing Multiple Active Learner vs ELLA + ATS vs ELLA + ATS + AL vs
# ELLA + AL
# This file runs ELLA + Active Task Selection
#####################
## Load all files
test_dat = util.add_bias(load_test())
pool_dat = util.add_bias(load_pool())
init_dat = util.add_bias(load_init())
init_size = util.dat_size(init_dat)
## Init ELLA Model with init set ##
ella_model = ELLA(eng, init_dat)
init_acc = ella_score(ella_model, test_dat)
test_acc = [init_acc]
learned_size = [init_size]
prof = Professor(init_dat, pool_dat, multi_t=True, random=True)
total_pool_size = prof.get_pool_size()
print "train pool size", total_pool_size
import math
NUM_CLUSTERS = 8
# This makes predictions based on the mean rating for each book in the
# training data. When there are no training data for a book, it
# defaults to the global mean.
pred_filename = 'predictor_age-kmeans1.csv'
train_filename = 'ratings-train.csv'
test_filename = 'ratings-test.csv'
book_filename = 'books.csv'
user_filename = 'users.csv'
training_data = util.load_train(train_filename)
test_queries = util.load_test(test_filename)
book_list = util.load_books(book_filename)
user_list = util.load_users(user_filename)
train_data = []
test_data = []
for datum in training_data:
if (random.randrange(2) == 0):
train_data.append(datum)
else:
test_data.append(datum)
# Compute the global mean rating for a fallback.
num_train = len(train_data)
mean_rating = float(sum(map(lambda x: x['rating'], train_data)))/num_train
ImageFile.LOAD_TRUNCATED_IMAGES = True
import shutil
from torchvision.models import vgg16
import warnings
warnings.filterwarnings('ignore')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
from dataset import DepthEigenDataset
from network import GlobalCoarseNet, LocalFineNet
from loss import ScaleInvariantLoss
import util
data_dir_test = Path('nyu/test')
bs = 32
dataloader_test, datalen_test = util.load_test(data_dir_test, bs)
print(datalen_test)
global_model = torch.load('models/global_model.pt')
global_model.eval()
local_model = torch.load('models/local_model.pt')
local_model.eval()
for i, samples in enumerate(dataloader_test):
rgbs = samples['rgb'].float().to(device)
depths = samples['depth'].float().to(device)
# results from global coarse network
with torch.no_grad():
global_output = global_model(rgbs).unsqueeze(1)