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utils.py
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utils.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import joblib
import tensorflow as tf
import keras_tuner as kt
import seaborn as sns
from typing import List
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score
class ParamsSearch:
'''
Class for hyperparameters search
Args:
X_train (np.ndarray): train data
y_train (np.ndarray): train labels
X_val (np.ndarray): validation data
y_val (np.ndarray): validation labels
num_classes (int): number of classes
Methods:
build(hp: kt.HyperParameters): build model
hyper_search(): search hyperparameters
'''
def __init__(self,
X_train: np.ndarray,
y_train: np.ndarray,
X_val: np.ndarray,
y_val: np.ndarray,
num_classes: int) -> None:
self.X_train = X_train
self.y_train = y_train
self.X_val = X_val
self.y_val = y_val
self.num_classes = num_classes
self.tuner = None
def build(self, hp: kt.HyperParameters) -> tf.keras.Sequential:
'''
Build model
Default parameters:
num_layers: 3
units: 128
activation: relu
dropout: False
lr: 0.0002
Args:
hp (kt.HyperParameters): hyperparameters
Returns:
model (tf.keras.Sequential): model
'''
model = tf.keras.Sequential()
for i in range(hp.Int("num_layers", 3, 6, default=3)):
model.add(
tf.keras.layers.Dense(
units=hp.Int(f"units_{i}", min_value=32, max_value=512, step=32, default=128),
activation=hp.Choice("activation", ["relu", "elu"], default="relu"),
)
)
model.add(tf.keras.layers.BatchNormalization())
if hp.Boolean("dropout", default=False):
model.add(tf.keras.layers.Dropout(rate=0.25))
model.add(tf.keras.layers.Dense(self.num_classes, activation="softmax"))
learning_rate = hp.Float("lr", min_value=1e-4, max_value=1e-2, sampling="log", default=0.0002)
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
loss="categorical_crossentropy",
metrics=["accuracy"],
)
return model
def hyper_search(self) -> None:
'''
Search hyperparameters
Max trials: 20 - number of models to train
Executions per trial: 2 - number of times to train each model
Epochs: 10 - number of epochs to train each model
'''
self.tuner = kt.RandomSearch(
hypermodel=self.build,
objective="val_accuracy",
max_trials=10,
executions_per_trial=2,
overwrite=True,
directory="./models",
project_name="hyperparametrs_search",
)
self.tuner.search(self.X_train, self.y_train, epochs=10, validation_data=(self.X_val, self.y_val))
class CoverTypeTrain:
'''
Class for training models
Methods:
load_data(): load data
knn(): train KNN model
rf(): train RF model
nn(): train NN model
heuristic(): heuristic model
'''
def __init__(self) -> None:
self.path = './data/covtype.data'
self.data = None
self.X_test, self.y_test = None, None
def load_data(self) -> None:
'''
Load data, change one-hot encoding to categorical,
drop one-hot encoded columns,
split data to train, validation and test
'''
self.data = pd.read_csv(self.path, header=None, sep=',')
self.y = self.data.iloc[:, -1].values
wilderness_areas = self.data.iloc[:, 10:14]
wilderness_areas.columns = list(range(1, 5))
self.data['10'] = wilderness_areas.idxmax(axis=1)
soil_types = self.data.iloc[:, 14:54]
soil_types.columns = list(range(1, 41))
self.data['11'] = soil_types.idxmax(axis=1)
self.data = self.data.drop(columns=self.data.columns[10:55])
self.X = self.data.values
#NOTE: split data to train and test
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=42)
#NOTE: split train data to train and validation
self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(self.X_train, self.y_train, test_size=0.1, random_state=42)
def knn(self,
neighbors: int = 3,
path_to_save: str = './models/knn.pkl') -> None:
'''
Method for training KNN model without scaling
Args:
neighbors (int): number of neighbors
path_to_save (str): path to save model
'''
model = Pipeline([
('knn', KNeighborsClassifier(n_neighbors=neighbors))
])
model.fit(self.X_train, self.y_train)
joblib.dump(model, path_to_save)
print(f'Model trained and saved to {path_to_save}')
def random_forest(self,
n_estimators: int = 100,
path_to_save: str = './models/rf.pkl') -> None:
'''
Method for training Random Forest model with scaling
Args:
n_estimators (int): number of estimators
path_to_save (str): path to save model
'''
model = Pipeline([
('scaler', StandardScaler()),
('random_forest', RandomForestClassifier(n_estimators=n_estimators))
])
model.fit(self.X_train, self.y_train)
joblib.dump(model, path_to_save)
print(f'Model trained and saved to {path_to_save}')
def nn(self,
path_to_save: str = './models/nn.h5',
search: bool = False,
plots: bool = False,
epochs: int = 30) -> None:
'''
Method for training NN model with scaling
Args:
path_to_save (str): path to save model
search (bool): search hyperparameters
plots (bool): show plots
epochs (int): number of epochs
'''
y_train_one_hot = tf.keras.utils.to_categorical(self.y_train)
y_val_one_hot = tf.keras.utils.to_categorical(self.y_val)
#NOTE: search on fraction of data for faster results
params = ParamsSearch(self.X_train[:100000],
y_train_one_hot[:100000],
self.X_val[:10000],
y_val_one_hot[:10000],
max(self.y_train)+1)
if search:
params.hyper_search()
best_hps = params.tuner.get_best_hyperparameters(5)
model = params.build(best_hps[0])
else:
#NOTE: pass a empty hyperparameters object to build the model with default hyperparameters
model = params.build(kt.HyperParameters())
model.fit(self.X_train, y_train_one_hot, epochs=epochs, validation_data=(self.X_val, y_val_one_hot))
if plots:
plt.figure()
plt.plot(model.history.history['accuracy'])
plt.plot(model.history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.savefig('./assets/accuracy_of_nn.png', facecolor='white')
plt.figure()
plt.plot(model.history.history['loss'])
plt.plot(model.history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.savefig('./assets/loss_of_nn.png', facecolor='white')
model.save(path_to_save)
@staticmethod
def heuristic(elevation_list: List[int]) -> List[float]:
'''
Heuristic model for predicting cover type based on elevation
Args:
elevation_list (List): list of elevations
Returns:
List: list of cover types
'''
elevation_ranges = {
1.0: (3100, 3300),
2.0: (2900, 3100),
3.0: (2500, 2700),
4.0: (1500, 2300),
5.0: (2700, 2900),
6.0: (2300, 2500),
7.0: (3300, 4000)
}
def predict(elevation: int) -> float:
for cover_type, elevation_range in elevation_ranges.items():
if elevation >= elevation_range[0] and elevation <= elevation_range[1]:
return cover_type
elif elevation < 1500:
return 4.0
elif elevation > 4000:
return 7.0
raise ValueError('Elevation out of range')
return [predict(elevation) for elevation in elevation_list]
class CoverTypeEvaluate:
'''
Class for evaluating models
Args:
X_test (np.array): test data
y_test (np.array): test labels
'''
def __init__(self,
X_test: np.ndarray,
y_test: np.ndarray,
knn_path: str = './models/knn.pkl',
rf_path: str = './models/rf.pkl',
nn_path: str = './models/nn.h5') -> None:
self.X_test = X_test
self.y_test = y_test
self.knn = joblib.load(knn_path)
self.rf = joblib.load(rf_path)
self.nn = tf.keras.models.load_model(nn_path)
self.heuristic = CoverTypeTrain.heuristic
def metrics(self, model_name: str, metric: str) -> float:
'''
Method for evaluating models
Args:
model_name (str): name of model
metric (str): metric to evaluate
Returns:
float: value of metric
'''
model = getattr(self, model_name)
if model_name == 'nn':
y_pred = np.argmax(model.predict(self.X_test), axis=1)
elif model_name == 'heuristic':
y_pred = self.heuristic(self.X_test[:, 0])
else:
y_pred = model.predict(self.X_test)
if metric == 'accuracy':
return accuracy_score(self.y_test, y_pred)
elif metric == 'precision':
return precision_score(self.y_test, y_pred, average='weighted')
elif metric == 'recall':
return recall_score(self.y_test, y_pred, average='weighted')
elif metric == 'f1':
return f1_score(self.y_test, y_pred, average='weighted')
elif metric == 'confusion_matrix':
return confusion_matrix(self.y_test, y_pred)
def plot_accuracies(self) -> None:
'''
Method for plotting accuracies of different models
'''
accuracies = {
'KNN': self.metrics(model_name='knn', metric='accuracy'),
'Random Forest': self.metrics(model_name='rf', metric='accuracy'),
'Neural Network': self.metrics(model_name='nn', metric='accuracy'),
'Heuristic': self.metrics(model_name='heuristic', metric='accuracy')
}
plt.figure(figsize=(8,6))
plt.bar(accuracies.keys(), accuracies.values(), color='blue')
plt.title('Accuracy Scores of Different Models')
plt.xlabel('Model')
plt.ylabel('Accuracy')
plt.ylim([0, 1.1])
for i, v in enumerate(accuracies.values()):
plt.text(i, v+0.01, str(round(v,2)), ha='center')
plt.savefig('./assets/accuracies.png', facecolor='white')
def plot_confusion_matrices(self) -> None:
'''
Method for plotting confusion matrices of different models
'''
confusion_matrices = {
'KNN': self.metrics(model_name='knn', metric='confusion_matrix'),
'Random Forest': self.metrics(model_name='rf', metric='confusion_matrix'),
'Neural Network': self.metrics(model_name='nn', metric='confusion_matrix'),
'Heuristic': self.metrics(model_name='heuristic', metric='confusion_matrix')
}
fig, axs = plt.subplots(2, 2, figsize=(12, 12))
fig.suptitle('Confusion Matrices of Different Models')
for i, (model_name, confusion_matrix) in enumerate(confusion_matrices.items()):
ax = axs[i//2, i%2]
ax.set_title(model_name)
sns.heatmap(confusion_matrix, annot=True, ax=ax, fmt='d')
ax.set_xlabel('Predicted')
ax.set_ylabel('Actual')
plt.savefig('./assets/confusion_matrices.png', facecolor='white')