import xgboost as xgb
from sklearn.model_selection import RandomizedSearchCV,GridSearchCV,ShuffleSplit
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from dataLoader import Dataloader
import matplotlib.pyplot as plt
import seaborn as sns
#Load Data
dl = Dataloader(normalization=True, select_features=["speed_max", "speed_mean", "speed_median", "speed_std"])
X_train, y_train = dl.getTrain()
X_test, y_test = dl.getTest()
X_validate, y_validate = dl.getValidate()
print(X_train.shape)
print(y_train.shape)
classes = {
"walk":0,
"bike":1,
"bus":2,
"taxi/car": 3,
"subway/train":4
}
inv_map = {v: k for k, v in classes.items()}
#Base Model
plt.axis('off')
plt.style.use('ggplot') # if want to use the default style, set 'classic'
plt.rcParams['ytick.right'] = True
plt.rcParams['ytick.labelright']= True
plt.rcParams['ytick.left'] = False
plt.rcParams['ytick.labelleft'] = False
plt.rcParams['font.family'] = 'Arial'
modelname = 'pre-1'
seed = 7
np.random.seed(seed)
# .............................................................................
dl = Dataloader(normalization=True, noise_removal=True)
x_train, y_train = dl.getTrain()
x_val, y_val = dl.getValidate()
#enc = OneHotEncoder(categories=[classes],handle_unknown='ignore',drop=[0])
y_train = to_categorical(y_train)
y_val = to_categorical(y_val)
x_train = np.expand_dims(x_train,axis=2)
x_val = np.expand_dims(x_val,axis=2)
dat = tf.convert_to_tensor(x_train)
lbl = tf.convert_to_tensor(y_train)
ds = tf.data.Dataset.from_tensor_slices((dat, lbl))
dataset = ds.shuffle(1000).batch(1).repeat()
dat = tf.convert_to_tensor(x_val)
lbl = tf.convert_to_tensor(y_val)