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2sigma-tool.py
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import matplotlib.pyplot as plt
import seaborn as sb
import pandas as pd
import numpy as np
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.linear_model import Ridge,
from sklearn.metrics import r2_score
train = pd.read_hdf('train.h5')
test_data = train.loc[train.timestamp > 905]
train = train.loc[train.timestamp <= 905]
# Feature Selection #
null_labels = [
'technical_21',
'technical_19',
'technical_27',
'technical_36',
'technical_35',
'technical_17',
'technical_43',
'technical_13',
'fundamental_33',
'technical_14',
'technical_33',
'fundamental_18',
'fundamental_48',
'fundamental_59',
'technical_9',
'technical_16',
'technical_42',
'technical_18',
'fundamental_42',
'fundamental_0',
'fundamental_7',
'fundamental_41',
'technical_41',
'fundamental_21',
'fundamental_19',
'technical_29',
'technical_24',
'derived_0',
'derived_1',
'fundamental_17',
'technical_3',
'fundamental_20',
'fundamental_32',
'fundamental_62',
'fundamental_25',
'technical_1',
'fundamental_58',
'derived_3',
'technical_5',
'fundamental_52',
'technical_10',
'technical_31',
'technical_25',
'technical_44',
'technical_28',
'fundamental_40',
'fundamental_27',
'fundamental_29',
'fundamental_43',
'fundamental_15',
'fundamental_30',
'fundamental_60',
'fundamental_16',
'fundamental_50',
'fundamental_44',
'fundamental_37',
'fundamental_14',
'fundamental_23',
'fundamental_55',
'fundamental_8',
'fundamental_63',
'fundamental_39',
'fundamental_54',
'derived_2',
'derived_4',
'fundamental_35',
'fundamental_34',
'fundamental_47',
'fundamental_51',
'fundamental_31',
'fundamental_49',
'fundamental_22',
'fundamental_9',
'fundamental_24',
'fundamental_57',
'fundamental_28',
'fundamental_61',
'fundamental_1',
'fundamental_6',
'fundamental_38',
'fundamental_5']
etr_features = ['y_past',
'tec20-30',
'technical_30',
'tec123',
'technical_43',
'technical_43_diff',
'tec123_past',
'technical_11_diff',
'technical_2_diff',
'technical_11',
'technical_20',
'technical_2',
'fundamental_25_nan',
'technical_14_diff',
'technical_21_diff',
'technical_9_nan',
'technical_40',
'technical_30_diff',
'technical_6_diff',
'technical_6',
'technical_17_diff',
'technical_17',
'technical_14',
'technical_7',
'technical_19',
'technical_44_nan',
'fundamental_27_nan',
'technical_18_nan',
'technical_28_nan',
'technical_21',
'technical_42_nan',
'technical_29_diff',
'technical_20_diff',
'technical_31_nan',
'fundamental_53',
'technical_24_nan',
'technical_36',
'technical_19_diff',
'technical_27',
'technical_29',
'technical_35',
'technical_22',
'technical_41_nan',
'fundamental_8',
'fundamental_21',
'fundamental_17_nan',
'technical_34',
'technical_16_nan',
'technical_27_diff',
'fundamental_33_nan',
'fundamental_58',
'derived_1_nan',
'technical_10',
'technical_25_nan',
'fundamental_18',
'fundamental_59',
'technical_40_diff',
'null_count',
'fundamental_5_nan',
'fundamental_48',
'fundamental_47_nan',
'technical_36_diff',
'fundamental_41_nan',
'fundamental_42_nan',
'fundamental_0_nan',
'fundamental_50',
'fundamental_40',
'technical_3_nan',
'fundamental_23',
'fundamental_49_nan',
'fundamental_36',
'technical_44',
'fundamental_2',
'fundamental_0',
'technical_41',
'fundamental_62_diff',
'technical_38_diff',
'fundamental_22_nan',
'technical_12',
'fundamental_62',
'technical_37_diff',
'fundamental_44',
'technical_29_nan',
'fundamental_24_nan',
'technical_10_nan',
'fundamental_46',
'technical_1',
'fundamental_54_nan',
'fundamental_0_diff',
'technical_12_diff',
'technical_35_diff',
'derived_3_nan',
'fundamental_63_nan',
'fundamental_31_nan',
'fundamental_40_nan',
'fundamental_35_nan',
'technical_3',
'fundamental_13']
seed = 17
excl = ['id', 'timestamp', 'y']
origin_features = [c for c in train.columns if c not in excl]
origin_features_exclude_y = [c for c in train.columns if c not in ['y']]
diff_features = [feature + '_diff' for feature in origin_features]
linear_features = ['technical_20_diff', 'tec20-30']
# End of Feature Selection #
d_mean = train.median(axis=0)
last_stamp = train.loc[train.timestamp == train.timestamp.max(), origin_features_exclude_y]
# add diffs #
def add_diff(data):
data.sort_values(['id', 'timestamp'], inplace=True)
data['id_diff'] = data.id.diff()
for feature in origin_features:
diff_tag = feature + '_diff'
data[diff_tag] = data[feature].diff()
d_mean[diff_tag] = 0
data.loc[data.id_diff != 0, diff_features] = 0
# end of diffs #
# add Nan tags #
def add_nan(data):
Nan_counts = data.isnull().sum(axis=1)
for feature in null_labels:
data[feature + '_nan'] = pd.isnull(data[feature])
d_mean[feature + '_nan'] = 0
data['null_count'] = Nan_counts
# end of Nan tags #
def predict_y_past(x):
w = np.array([-8.36489105, 9.19544792]).T
return x.dot(w) - 0.00020021698404804056
ymean_dict = dict(train.groupby(["id"])["y"].median())
print('Processing data...')
train = train
add_diff(train)
add_nan(train)
train = train.fillna(d_mean)
train['tec20-30'] = train.technical_20 - train.technical_30
train['tec123'] = train['tec20-30'] + train.technical_13
train['tec123_past'] = train.tec123.shift()
train['y_past'] = train.y.shift()
train.loc[train.id_diff != 0, ['tec123_past', 'y_past']] = 0
low_y_cut = -0.075
high_y_cut = 0.075
y_above_cut = (train.y > high_y_cut)
y_below_cut = (train.y < low_y_cut)
y_within_cut = (~y_above_cut & ~y_below_cut)
# Generate models...
ridge_1 = Ridge()
ridge_2 = Ridge()
etr = ExtraTreesRegressor(n_estimators=1, max_depth=6, min_samples_leaf=30,
max_features=0.6, n_jobs=-1, random_state=seed, verbose=0)
print('Training Linear Model...\n', len(linear_features), 'features')
ridge_2.fit(train.loc[y_within_cut, linear_features], train.loc[y_within_cut, 'y'])
ridge_1.fit(np.array(train.loc[y_within_cut, linear_features[0]]
).reshape(-1, 1), train.loc[y_within_cut, 'y'])
print('Training ETR Model...\n', len(etr_features), 'features')
etr.fit(train.loc[y_within_cut, etr_features], train.loc[y_within_cut, 'y'])
# end of Generate models.
# full_df = pd.read_hdf('../input/train.h5')
reward = []
# predicting...
print('Predicting...')
r_true = []
y_lr_1_p = 0
y_lr_2_p = 0
a = 0.5
b = 0.5
timestamp = 906
y_pred_acc = []
y_act_acc = []
while timestamp <= 1812:
test = test_data.loc[test_data.timestamp == timestamp]
test = pd.concat([test, last_stamp])
add_diff(test)
test['tec20-30'] = test.technical_20 - test.technical_30
test['tec123'] = test['tec20-30'] + test.technical_13
test['tec123_past'] = test.tec123.shift()
test.loc[test.id_diff != 0, 'tec123_past'] = 0
test = test.loc[test.timestamp == timestamp]
test.sort_index(inplace=True)
add_nan(test)
test.fillna(d_mean, inplace=True)
test['y_past'] = predict_y_past(test[['tec123_past', 'tec123']])
test.fillna(0, inplace=True)
last_stamp = test.loc[test.timestamp == timestamp, origin_features_exclude_y]
y_etr = etr.predict(test[etr_features])
y_act = test.loc[test.timestamp == timestamp, 'y']
y_lr_2 = ridge_2.predict(test[linear_features]).clip(low_y_cut, high_y_cut)
y_lr_1 = ridge_1.predict(np.array(test[linear_features[0]]
).reshape(-1, 1)).clip(low_y_cut, high_y_cut)
y = y_lr_1 * 0.05 + y_lr_2 * 0.2 + y_etr * 0.75
r_1 = r2_score(y_act, y_lr_1)
r_2 = r2_score(y_act, y_lr_2)
r_etr = r2_score(y_act, y_etr)
R = r2_score(y_act, y)
y_pred_acc.extend(list(y))
y_act_acc.extend(list(y_act))
score = r2_score(np.array(y_act_acc), np.array(y_pred_acc))
reward.append(score)
timestamp += 1
if timestamp % 100 == 0:
print('timestamp:', timestamp, '---->', score)
print('final r2_score:', reward[-1])
plt.figure(figsize=(15, 8))
plt.plot(range(906, test.timestamp.max() + 1), reward)
plt.plot([905, 1801], [0, 0], 'k--')
plt.show()