Kaggle for earthquake

## Types of models used
-lightGBM
-Genetic programming
-SVM 

Predicting earthquakes has long been thought to be near-impossible. But same has been said about many other things, I still remember how 15 years ago my IT teacher in high school was saying that speech recognition can never be done reliably by a computer, and today it's more reliable than a human. So who knows, maybe this competition will be a major step in solving another "impossible" problem. Importance of predicting earthquakes is hard to underestimate - selected years of our century claimed hundreds of thousands of causalities. Being able to predict earthquakes could allow us to better protect human life and property.

Supervised learning

How was the data generated? Why is this important?

-Show the 1st 2%, then zoom in
-audio quakes right before the earthquake
-Add some features?
-K Folds for Validation
- Types of models used (LightGBM, genetic programming, SVM)

!pip install kaggle

from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))
  
# Then move kaggle.json into the folder where the API expects to find it.
!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json

!kaggle competitions list

!kaggle competitions download -c LANL-Earthquake-Prediction

!unzip train.csv.zip

!ls

import pandas as pd
import numpy as np

!pip install numpy==1.15.0

train_df = pd.read_csv('train.csv', dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})

#train = pd.read_csv('train.csv', iterator=True, chunksize=150_000, dtype={'acoustic_data': np.int16, 'time_to_failure': np.float64})

##print("Train: rows:{} cols:{}".format(train_df.shape[0], train_df.shape[1]))
def gen_features(X):
    strain = []
    strain.append(X.mean())
    strain.append(X.std())
    strain.append(X.min())
    strain.append(X.max())
    strain.append(X.mad())
    strain.append(X.kurtosis())
    strain.append(X.skew())
    strain.append(np.quantile(X,0.01))
    strain.append(np.quantile(X,0.05))
    strain.append(np.quantile(X,0.95))
    strain.append(np.quantile(X,0.99))
    strain.append(np.abs(X).max())
    strain.append(np.abs(X).mean())
    strain.append(np.abs(X).std())
    return pd.Series(strain)
  
X_train = pd.DataFrame()
y_train = pd.Series()
for df in train:
    ch = gen_features(df['acoustic_data'])
    X_train = X_train.append(ch, ignore_index=True)
    y_train = y_train.append(pd.Series(df['time_to_failure'].values[-1]))

X_train.describe()

#!pip install catboost
from catboost import CatBoostRegressor, Pool


train_pool = Pool(X_train, y_train)

m = CatBoostRegressor(iterations=10000, loss_function='MAE', boosting_type='Ordered')
m.fit(X_train, y_train, silent=True)
m.best_score_

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.svm import NuSVR, SVR


scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)

parameters = [{'gamma': [0.001, 0.005, 0.01, 0.02, 0.05, 0.1],
               'C': [0.1, 0.2, 0.25, 0.5, 1, 1.5, 2]}]
               #'nu': [0.75, 0.8, 0.85, 0.9, 0.95, 0.97]}]

reg1 = GridSearchCV(SVR(kernel='rbf', tol=0.01), parameters, cv=5, scoring='neg_mean_absolute_error')
reg1.fit(X_train_scaled, y_train.values.flatten())
y_pred1 = reg1.predict(X_train_scaled)

print("Best CV score: {:.4f}".format(reg1.best_score_))
print(reg1.best_params_)

from sklearn.kernel_ridge import KernelRidge

parameters = [{'gamma': np.linspace(0.001, 0.1, 10),
               'alpha': [0.005, 0.01, 0.02, 0.05, 0.1]}]

reg2 = GridSearchCV(KernelRidge(kernel='rbf'), parameters, cv=5, scoring='neg_mean_absolute_error')
reg2.fit(X_train_scaled, y_train.values.flatten())
y_pred2 = reg2.predict(X_train_scaled)

print("Best CV score: {:.4f}".format(reg2.best_score_))
print(reg2.best_params_)

import lightgbm as lgb
from tqdm import tqdm_notebook as tqdm
import random


fixed_params = {
    'objective': 'regression_l1',
    'boosting': 'gbdt',
    'verbosity': -1,
    'random_seed': 19
}

param_grid = {
    'learning_rate': [0.1, 0.08, 0.05, 0.01],
    'num_leaves': [32, 46, 52, 58, 68, 72, 80, 92],
    'max_depth': [3, 4, 5, 6, 8, 12, 16, -1],
    'feature_fraction': [0.8, 0.85, 0.9, 0.95, 1],
    'subsample': [0.8, 0.85, 0.9, 0.95, 1],
    'lambda_l1': [0, 0.1, 0.2, 0.4, 0.6, 0.9],
    'lambda_l2': [0, 0.1, 0.2, 0.4, 0.6, 0.9],
    'min_data_in_leaf': [10, 20, 40, 60, 100],
    'min_gain_to_split': [0, 0.001, 0.01, 0.1],
}

best_score = 999
dataset = lgb.Dataset(X_train, label=y_train)  # no need to scale features

for i in tqdm(range(200)):
    params = {k: random.choice(v) for k, v in param_grid.items()}
    params.update(fixed_params)
    result = lgb.cv(params, dataset, nfold=5, early_stopping_rounds=50,
                    num_boost_round=20000, stratified=False)
    
    if result['l1-mean'][-1] < best_score:
        best_score = result['l1-mean'][-1]
        best_params = params
        best_nrounds = len(result['l1-mean'])

print("Best mean score: {:.4f}, num rounds: {}".format(best_score, best_nrounds))
print(best_params)
reg3 = lgb.train(best_params, dataset, best_nrounds)
y_pred3 = reg3.predict(X_train)

plt.tight_layout()
f = plt.figure(figsize=(12, 6))
f.add_subplot(1, 3, 1)
plt.scatter(y_train.values.flatten(), y_pred1)
plt.title('SVR', fontsize=16)
plt.xlim(0, 20)
plt.ylim(0, 20)
plt.xlabel('actual', fontsize=12)
plt.ylabel('predicted', fontsize=12)
plt.plot([(0, 0), (20, 20)], [(0, 0), (20, 20)])

f.add_subplot(1, 3, 2)
plt.scatter(y_train.values.flatten(), y_pred2)
plt.title('Kernel Ridge', fontsize=16)
plt.xlim(0, 20)
plt.ylim(0, 20)
plt.xlabel('actual', fontsize=12)
plt.plot([(0, 0), (20, 20)], [(0, 0), (20, 20)])

f.add_subplot(1, 3, 3)
plt.scatter(y_train.values.flatten(), y_pred3)
plt.title('Gradient boosting', fontsize=16)
plt.xlim(0, 20)
plt.ylim(0, 20)
plt.xlabel('actual', fontsize=12)
plt.plot([(0, 0), (20, 20)], [(0, 0), (20, 20)])
plt.show(block=True)

# second plot
plt.figure(figsize=(10, 5))
plt.plot(y_train.values.flatten(), color='blue', label='y_train')
plt.plot(y_pred1, color='orange', label='SVR')
plt.legend()
plt.title('SVR predictions vs actual')

# third plot
plt.figure(figsize=(10, 5))
plt.plot(y_train.values.flatten(), color='blue', label='y_train')
plt.plot(y_pred2, color='gray', label='KernelRidge')
plt.legend()
plt.title('Kernel Ridge predictions vs actual')

# fourth plot
plt.figure(figsize=(10, 5))
plt.plot(y_train.values.flatten(), color='blue', label='y_train')
plt.plot(y_pred3, color='green', label='Gradient boosting')
plt.legend()
plt.title('GBDT predictions vs actual')

import matplotlib.pyplot as plt


train_ad_sample_df = X_train['acoustic_data'].values[::100]
train_ttf_sample_df = X_train['time_to_failure'].values[::100]

def plot_acc_ttf_data(train_ad_sample_df, train_ttf_sample_df, title="Acoustic data and time to failure: 1% sampled data"):
    fig, ax1 = plt.subplots(figsize=(12, 8))
    plt.title(title)
    plt.plot(train_ad_sample_df, color='r')
    ax1.set_ylabel('acoustic data', color='r')
    plt.legend(['acoustic data'], loc=(0.01, 0.95))
    ax2 = ax1.twinx()
    plt.plot(train_ttf_sample_df, color='b')
    ax2.set_ylabel('time to failure', color='b')
    plt.legend(['time to failure'], loc=(0.01, 0.9))
    plt.grid(True)

plot_acc_ttf_data(train_ad_sample_df, train_ttf_sample_df)
del train_ad_sample_df
del train_ttf_sample_df

train_ad_sample_df = train_df['acoustic_data'].values[:6291455]
train_ttf_sample_df = train_df['time_to_failure'].values[:6291455]
plot_acc_ttf_data(train_ad_sample_df, train_ttf_sample_df, title="Acoustic data and time to failure: 1% of data")
del train_ad_sample_df
del train_ttf_sample_df

def gen_features(X):
    strain = []
    strain.append(X.mean())
    strain.append(X.std())
    strain.append(X.min())
    strain.append(X.max())
    strain.append(X.mad())
    strain.append(X.kurtosis())
    strain.append(X.skew())
    strain.append(np.quantile(X,0.01))
    strain.append(np.quantile(X,0.05))
    strain.append(np.quantile(X,0.95))
    strain.append(np.quantile(X,0.99))
    strain.append(np.abs(X).max())
    strain.append(np.abs(X).mean())
    strain.append(np.abs(X).std())
    return pd.Series(strain)

= pd.DataFrame()
y_train = pd.Series()
for df in train_df:
    ch = gen_features(train_df['acoustic_data'])
    X_train = X_train.append(ch, ignore_index=True)
    y_train = y_train.append(pd.Series(df['time_to_failure'].values[-1]))