https://youtu.be/PWZLhr3FfIM

Classification Problem

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
In [0]:
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import f_classif, f_regression
from sklearn.feature_selection import SelectKBest, SelectPercentile
In [3]:
data = pd.read_csv('/content/drive/My Drive/kaggle/Learning_mutual_information/data/santander-train.csv', nrows=20000)
data.head()
Out[3]:
ID var3 var15 imp_ent_var16_ult1 imp_op_var39_comer_ult1 imp_op_var39_comer_ult3 imp_op_var40_comer_ult1 imp_op_var40_comer_ult3 imp_op_var40_efect_ult1 imp_op_var40_efect_ult3 imp_op_var40_ult1 imp_op_var41_comer_ult1 imp_op_var41_comer_ult3 imp_op_var41_efect_ult1 imp_op_var41_efect_ult3 imp_op_var41_ult1 imp_op_var39_efect_ult1 imp_op_var39_efect_ult3 imp_op_var39_ult1 imp_sal_var16_ult1 ind_var1_0 ind_var1 ind_var2_0 ind_var2 ind_var5_0 ind_var5 ind_var6_0 ind_var6 ind_var8_0 ind_var8 ind_var12_0 ind_var12 ind_var13_0 ind_var13_corto_0 ind_var13_corto ind_var13_largo_0 ind_var13_largo ind_var13_medio_0 ind_var13_medio ind_var13 ... saldo_medio_var5_ult1 saldo_medio_var5_ult3 saldo_medio_var8_hace2 saldo_medio_var8_hace3 saldo_medio_var8_ult1 saldo_medio_var8_ult3 saldo_medio_var12_hace2 saldo_medio_var12_hace3 saldo_medio_var12_ult1 saldo_medio_var12_ult3 saldo_medio_var13_corto_hace2 saldo_medio_var13_corto_hace3 saldo_medio_var13_corto_ult1 saldo_medio_var13_corto_ult3 saldo_medio_var13_largo_hace2 saldo_medio_var13_largo_hace3 saldo_medio_var13_largo_ult1 saldo_medio_var13_largo_ult3 saldo_medio_var13_medio_hace2 saldo_medio_var13_medio_hace3 saldo_medio_var13_medio_ult1 saldo_medio_var13_medio_ult3 saldo_medio_var17_hace2 saldo_medio_var17_hace3 saldo_medio_var17_ult1 saldo_medio_var17_ult3 saldo_medio_var29_hace2 saldo_medio_var29_hace3 saldo_medio_var29_ult1 saldo_medio_var29_ult3 saldo_medio_var33_hace2 saldo_medio_var33_hace3 saldo_medio_var33_ult1 saldo_medio_var33_ult3 saldo_medio_var44_hace2 saldo_medio_var44_hace3 saldo_medio_var44_ult1 saldo_medio_var44_ult3 var38 TARGET
0 1 2 23 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.00 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 39205.170000 0
1 3 2 34 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 ... 0.00 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 300.0 122.22 300.0 240.75 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 49278.030000 0
2 4 2 23 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 3.00 2.07 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.00 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 67333.770000 0
3 8 2 37 0.0 195.0 195.0 0.0 0.0 0 0 0.0 195.0 195.0 0.0 0.0 195.0 0.0 0.0 195.0 0.0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 91.56 138.84 0.0 0.0 0.0 0.0 0.0 0.0 0.00 0.00 0.0 0.00 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 64007.970000 0
4 10 2 39 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 ... 40501.08 13501.47 0.0 0.0 0.0 0.0 0.0 0.0 85501.89 85501.89 0.0 0.00 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0 0 0.0 0.0 0.0 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 117310.979016 0

5 rows × 371 columns

In [4]:
x = data.drop('TARGET', axis=1)
y = data['TARGET']

x.shape, y.shape
Out[4]:
((20000, 370), (20000,))
In [0]:
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0, stratify=y)

Remove Constant, Quasi Constant and Correlated Features

In [0]:
# remove constant and quasi constant features
cosntant_filter = VarianceThreshold(threshold=0.01)
cosntant_filter.fit(x_train)
x_train_filter = cosntant_filter.transform(x_train)
x_test_filter = cosntant_filter.transform(x_test)
In [7]:
x_train_filter.shape, x_test_filter.shape
Out[7]:
((16000, 245), (4000, 245))
In [0]:
# remove duplicate feature
x_train_T = x_train_filter.T
x_test_T = x_test_filter.T
In [0]:
x_train_T = pd.DataFrame(x_train_T)
x_test_T = pd.DataFrame(x_test_T)
In [10]:
x_train_T.duplicated().sum()
Out[10]:
18
In [0]:
duplicated_features = x_train_T.duplicated()
In [0]:
features_to_keep = [not index for index in duplicated_features]

x_train_unique = x_train_T[features_to_keep].T
x_test_unique = x_test_T[features_to_keep].T
In [13]:
x_train_unique.shape, x_train.shape
Out[13]:
((16000, 227), (16000, 370))
In [15]:
x_train_unique.head()
Out[15]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 38 40 41 ... 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244
0 17282.0 2.0 24.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 ... 0.0 0.0 0.0 0.0 3.0 1.89 3.0 2.64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 63200.700000
1 38270.0 2.0 23.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 88640.610000
2 31526.0 2.0 45.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 ... 3.0 0.0 0.0 3.0 3.0 0.33 3.0 2.10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 96314.160000
3 38737.0 2.0 29.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.00 0.0 0.00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 117568.020000
4 16469.0 2.0 23.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 ... 3.0 0.0 0.0 3.0 30.0 9.69 30.0 23.22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 117310.979016

5 rows × 227 columns

do F-Test

In [16]:
# f_classifで独立変数毎の従属変数とのF値とp値を求める
sel = f_classif(x_train_unique, y_train)
sel
# 戻り値：
# １dimension-独立変数毎のF値、2dimension-独立変数毎のp値
Out[16]:
(array([3.42911520e-01, 1.22929093e+00, 1.61291330e+02, 4.01025132e-01,
        8.37661151e-01, 2.39279390e-03, 4.41633351e-02, 1.36337510e-01,
        1.84647123e+00, 2.03640367e+00, 7.98057954e-03, 1.14063993e+00,
        6.32266614e-03, 1.55626237e+01, 1.53553790e+01, 1.28615978e+01,
        1.61834746e+01, 1.59638013e+01, 1.21977511e+01, 9.03776687e-02,
        1.00443179e+00, 1.53946148e+01, 2.50428951e+02, 2.98696944e+01,
        1.06266841e+01, 2.63630437e+01, 1.66417611e+01, 3.13699473e+01,
        2.47256550e+01, 2.60021376e+01, 3.26742018e+01, 9.94259060e+00,
        1.48208220e+01, 1.50040146e+01, 1.34739830e+01, 7.03118653e+00,
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        1.66910747e+00, 1.21138302e+01, 1.10928892e+01, 1.00443179e+00,
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        8.46910021e-02, 2.36606015e-01, 5.89389684e+00, 2.77252663e-01,
        4.15074036e-01, 1.44558159e-01, 1.17723957e+00, 9.22407334e-01,
        1.45895164e+01, 1.86656969e+00, 5.43234215e+00, 1.86971763e-02,
        3.09123385e+02, 7.12088878e+00, 1.49660894e+01, 2.43275497e+01,
        4.52466899e+00, 2.03980835e-01, 5.87673213e-03, 4.98543138e-02,
        5.16359722e-02, 1.09646850e-01, 2.06155459e+00, 2.99184059e+00,
        2.21995621e-02, 1.13858713e-01, 1.14255501e+01, 1.13785982e+01,
        1.19082872e+01, 1.18528440e+01, 2.65465286e-02, 1.52894509e-01,
        4.63685902e+00, 2.10080736e+00, 1.65523608e-01, 2.16891078e-01,
        1.40302586e+00, 5.48359285e-01, 6.35218588e-02, 4.88987865e+00,
        2.49656443e+00, 4.58216058e+00, 4.15099427e+00, 4.56305342e-01,
        1.66491238e-01, 3.90777488e-01, 3.50953637e-01, 5.52484208e+00,
        2.37194124e+00, 7.35792170e+00, 7.47930913e+00, 1.19139338e+01,
        3.63667170e+00, 1.46817492e+01, 1.40921857e+01, 2.55113543e+00,
        7.93363123e-01, 2.95584767e+00, 2.83339311e+00, 4.73780486e-02,
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        1.11726086e-01, 1.17106404e-01, 3.13117156e-01, 1.24267517e-01,
        2.84184735e-01, 3.29540269e-01, 1.12297080e+01]),
 array([5.58161700e-01, 2.67561647e-01, 8.89333290e-37, 5.26569363e-01,
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        7.71041949e-01, 6.26674911e-01, 1.52044006e-02, 5.98514898e-01,
        5.19414532e-01, 7.03796034e-01, 2.77935032e-01, 3.36858193e-01,
        1.34160887e-04, 1.71887678e-01, 1.97795129e-02, 8.91239914e-01,
        1.49493801e-68, 7.62677506e-03, 1.09894509e-04, 8.20840983e-07,
        3.34247932e-02, 6.51532760e-01, 9.38895107e-01, 8.23319823e-01,
        8.20243529e-01, 7.40550887e-01, 1.51075546e-01, 8.37042875e-02,
        8.81559318e-01, 7.35797526e-01, 7.26140977e-04, 7.44713683e-04,
        5.60290090e-04, 5.77206713e-04, 8.70574777e-01, 6.95789674e-01,
        3.13071224e-02, 1.47240985e-01, 6.84126595e-01, 6.41425380e-01,
        2.36235196e-01, 4.58999733e-01, 8.01016915e-01, 2.70286705e-02,
        1.14114721e-01, 3.23215275e-02, 4.16265059e-02, 4.99365521e-01,
        6.83254631e-01, 5.31899920e-01, 5.53582168e-01, 1.87603701e-02,
        1.23553129e-01, 6.68393143e-03, 6.24807382e-03, 5.58595551e-04,
        5.65377099e-02, 1.27759206e-04, 1.74681308e-04, 1.10234773e-01,
        3.73098519e-01, 8.55867690e-02, 9.23426211e-02, 8.27692918e-01,
        8.36351110e-01, 8.02680255e-01, 8.04332893e-01, 8.11179263e-01,
        7.82079079e-01, 7.39775741e-01, 7.21990132e-01, 8.36970444e-01,
        7.38191886e-01, 7.32198763e-01, 5.75781477e-01, 7.24455979e-01,
        5.93978825e-01, 5.65937990e-01, 8.06846135e-04]))
In [0]:
# p値が0.05以下の独立変数を抽出する
p_values = pd.Series(sel[1])
p_values.index = x_train_unique.columns
p_values.sort_values(ascending=True, inplace=True)
In [20]:
p_values.plot.bar(figsize=(16, 5))
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f7bab5c6208>
In [0]:
p_values = p_values[p_values < 0.05]
In [23]:
p_values.index
Out[23]:
Int64Index([ 40, 182,  86,  22, 101,  51,   2, 127,  49,  91,  30,  27,  61,
             52,  23,  85,  56,  25,  54,  29,  58,  28,  57, 185, 119, 111,
             26,  55,  16,  17,  13,  21,  14,  69,  33, 184,  32,  68, 223,
            178, 109, 224,  36,  34,  15,  18,  44, 168, 221, 198, 199, 100,
            196, 197, 244,  46,  24,  53,  62,  31, 125,  38, 144,  50, 108,
            220, 115, 219, 183,  35,  98, 172,  60,  59, 217, 180,  95,  92,
            166,  72, 105, 209, 202, 211, 186, 212,  70, 110],
           dtype='int64')
In [0]:
x_train_p = x_train_unique[p_values.index]
x_test_p = x_test_unique[p_values.index]

Build the classifiers and compare the performance

In [0]:
def run_randomForest(x_train, x_test, y_train, y_test):
    clf = RandomForestClassifier(n_estimators=100, random_state=0, n_jobs=-1)
    clf.fit(x_train, y_train)
    y_pred = clf.predict(x_test)
    print('Accuracy: ', accuracy_score(y_test, y_pred))
In [26]:
%%time
run_randomForest(x_train_p, x_test_p, y_train, y_test)

Accuracy:  0.953
CPU times: user 3.32 s, sys: 34.7 ms, total: 3.35 s
Wall time: 1.86 s
In [27]:
%%time
run_randomForest(x_train, x_test, y_train, y_test)

Accuracy:  0.9585
CPU times: user 5.99 s, sys: 32.3 ms, total: 6.03 s
Wall time: 3.19 s
In [0]: