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%% Cell type:code id:f097ad55 tags: %% Cell type:code id:f097ad55 tags:
``` python ``` python
import warnings import warnings
warnings.filterwarnings('ignore') warnings.filterwarnings('ignore')
import pandas as pd import pandas as pd
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
#from sklearn.model_selection import cross_val_score #from sklearn.model_selection import cross_val_score
#from sklearn.metrics import accuracy_score #from sklearn.metrics import accuracy_score
#import sklearn.metrics as metrics #import sklearn.metrics as metrics
#from sklearn.metrics import auc #from sklearn.metrics import auc
from sklearn.metrics import RocCurveDisplay from sklearn.metrics import RocCurveDisplay
#from sklearn.model_selection import KFold #from sklearn.model_selection import KFold
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from imblearn.over_sampling import SMOTE from imblearn.over_sampling import SMOTE
from sklearn.linear_model import Lasso from sklearn.linear_model import Lasso
import xgboost as xgb import xgboost as xgb
from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV
import pandas as pd import pandas as pd
import numpy as np import numpy as np
#np.random.seed(7) #np.random.seed(7)
``` ```
%% Cell type:markdown id:73b6611a tags: %% Cell type:markdown id:73b6611a tags:
# Data Preprocessing # Data Preprocessing
%% Cell type:code id:0eeb7a35 tags: %% Cell type:code id:0eeb7a35 tags:
``` python ``` python
df = pd.read_csv("DS/miRNA_DS_preprocessed_data.csv") df = pd.read_csv("DS/miRNA_DS_preprocessed_data.csv")
``` ```
%% Cell type:code id:6e7836e1 tags: %% Cell type:code id:6e7836e1 tags:
``` python ``` python
df.shape df.shape
``` ```
%% Output %% Output
(230, 239) (230, 239)
%% Cell type:code id:683b63ce tags: %% Cell type:code id:683b63ce tags:
``` python ``` python
df = df.T df = df.T
``` ```
%% Cell type:code id:2e78017d tags: %% Cell type:code id:2e78017d tags:
``` python ``` python
#Transform the input data #Transform the input data
df.rename(columns=df.iloc[0], inplace = True) df.rename(columns=df.iloc[0], inplace = True)
df.drop(df.index[0], inplace = True) df.drop(df.index[0], inplace = True)
df=df.reset_index() df=df.reset_index()
``` ```
%% Cell type:code id:1647a959 tags:
``` python
df
```
%% Output
index dmr_3 dmr_31a dmr_6 ebv-miR-BART13 hsa-let-7c \
0 GSM1069774 0.732675 -0.242559 0.577801 -4.469532 1.195899
1 GSM1069775 0.249772 -0.655514 0.104933 -5.209572 0.498366
2 GSM1069776 0.400779 -0.597444 0.232702 -4.952808 1.081166
3 GSM1069777 0.380263 -0.900491 0.243207 -4.892073 -0.023958
4 GSM1069778 0.422207 -0.414831 -0.000781 -5.139127 1.077485
.. ... ... ... ... ... ...
233 GSM1070007 0.98797 -0.118186 0.750199 -4.572984 0.696251
234 GSM1070008 -0.194781 -0.710519 -0.700226 -5.651293 0.742722
235 GSM1070009 0.21218 -0.284657 -0.32472 -4.800142 1.0062
236 GSM1070010 0.330997 -0.19446 -0.206405 -4.840442 1.521159
237 GSM1070011 0.474815 0.043697 -0.102511 -4.849285 1.239637
hsa-let-7d-5p hsa-let-7i-5p hsa-miR-100-5p hsa-miR-101-3p ... \
0 -0.334742 0.89199 -2.089223 -2.757097 ...
1 -0.194772 0.637863 -2.357572 -2.196884 ...
2 0.249982 1.45018 -1.138559 -1.802774 ...
3 -0.980435 1.071857 -2.077406 -2.11406 ...
4 -0.684875 0.724751 -0.689096 -1.182558 ...
.. ... ... ... ... ...
233 -1.089669 0.826 -1.604393 -2.87334 ...
234 -0.964527 0.570816 -1.046029 -1.840615 ...
235 -0.141699 0.80704 -0.993146 -0.823621 ...
236 -0.424901 0.886358 -0.031455 -1.584939 ...
237 -0.704124 0.698355 -0.414715 -1.721427 ...
hsv2-miR-H24 hsv2-miR-H25 hsv2-miR-H6 hur_1 hur_2 hur_4 \
0 -3.956004 -3.936689 -4.099346 6.98856 7.041557 3.822267
1 -4.334103 -4.561624 -4.719714 6.774479 6.862654 3.529789
2 -4.550077 -4.40729 -4.621278 6.808404 6.75867 3.496675
3 -4.018911 -4.203106 -3.938707 6.524773 6.497959 3.541502
4 -3.690971 -4.332452 -4.178727 6.562608 6.529399 3.305132
.. ... ... ... ... ... ...
233 -2.163581 -2.15805 -2.302647 7.093144 7.150126 3.899704
234 -4.507365 -4.23831 -4.63219 6.18658 6.232722 2.788619
235 -2.737709 -2.644713 -3.253632 6.505956 6.548781 3.12575
236 -3.292034 -2.941633 -3.939222 6.790132 6.829164 3.365475
237 -3.378909 -2.909732 -3.510667 6.80237 6.784016 3.514036
hur_5 hur_6 miRNABrightCorner30 mr_1
0 -2.268209 5.114399 2.017444 1.640437
1 -2.656642 4.327117 2.022346 0.79426
2 -2.676555 4.616284 1.498011 1.584544
3 -3.073553 4.581648 0.789822 1.255367
4 -2.964948 4.487481 1.219583 0.951615
.. ... ... ... ...
233 -2.954284 5.505105 2.457963 2.142301
234 -3.103706 4.340513 0.232713 1.067806
235 -2.917537 4.838599 0.863574 1.203499
236 -2.736411 5.185601 0.846454 1.604729
237 -2.931018 4.798139 2.08952 1.597958
[238 rows x 231 columns]
%% Cell type:code id:4c50c510 tags: %% Cell type:code id:4c50c510 tags:
``` python ``` python
metadata = pd.read_csv("DS/miRNA_DS_metadata_col_info.csv") metadata = pd.read_csv("DS/miRNA_DS_metadata_col_info.csv")
``` ```
%% Cell type:code id:6730cf89 tags: %% Cell type:code id:6730cf89 tags:
``` python ``` python
df= df.merge(metadata, left_on="index", right_on= "Unnamed: 0") df= df.merge(metadata, left_on="index", right_on= "Unnamed: 0")
``` ```
%% Cell type:code id:7a8ad8ad tags: %% Cell type:code id:7a8ad8ad tags:
``` python ``` python
df['title0'] = df['title0'].replace('(?i)mucosa|normal|healthy', 0, regex=True) df['title0'] = df['title0'].replace('(?i)mucosa|normal|healthy', 0, regex=True)
``` ```
%% Cell type:code id:a8cf8643 tags: %% Cell type:code id:a8cf8643 tags:
``` python ``` python
df['title0'] = df['title0'].replace('(?i)Tumor|Cancer|carcinoma', 1, regex=True) df['title0'] = df['title0'].replace('(?i)Tumor|Cancer|carcinoma', 1, regex=True)
``` ```
%% Cell type:code id:5c852a3f tags: %% Cell type:code id:5c852a3f tags:
``` python ``` python
df['title0'].value_counts() df['title0'].value_counts()
``` ```
%% Output %% Output
title0 title0
1 119 1 119
0 119 0 119
Name: count, dtype: int64 Name: count, dtype: int64
%% Cell type:code id:f5d203aa tags: %% Cell type:code id:f5d203aa tags:
``` python ``` python
df = df[pd.to_numeric(df['title0'], errors='coerce').notnull()]#remove all non-numeric data from the column. df = df[pd.to_numeric(df['title0'], errors='coerce').notnull()]#remove all non-numeric data from the column.
``` ```
%% Cell type:code id:523bdaa6 tags: %% Cell type:code id:523bdaa6 tags:
``` python ``` python
df= df.drop(['index', 'Unnamed: 0'], axis=1) df= df.drop(['index', 'Unnamed: 0'], axis=1)
``` ```
%% Cell type:code id:46a6fb36 tags: %% Cell type:code id:46a6fb36 tags:
``` python ``` python
df= df.rename(columns={"title0": "index"}) df= df.rename(columns={"title0": "index"})
``` ```
%% Cell type:code id:e26f88c5 tags: %% Cell type:code id:e26f88c5 tags:
``` python ``` python
df['index'].value_counts() df['index'].value_counts()
``` ```
%% Output %% Output
index index
1 119 1 119
0 119 0 119
Name: count, dtype: int64 Name: count, dtype: int64
%% Cell type:code id:fbaf2507 tags: %% Cell type:code id:fbaf2507 tags:
``` python ``` python
df= df.apply(pd.to_numeric) df= df.apply(pd.to_numeric)
``` ```
%% Cell type:code id:f3f7adb5 tags: %% Cell type:code id:f3f7adb5 tags:
``` python ``` python
df['index'].value_counts() df['index'].value_counts()
``` ```
%% Output %% Output
index index
1 119 1 119
0 119 0 119
Name: count, dtype: int64 Name: count, dtype: int64
%% Cell type:code id:6a50f416 tags: %% Cell type:code id:6a50f416 tags:
``` python ``` python
X=df.drop("index",axis=1) X=df.drop("index",axis=1)
y=df['index'] y=df['index']
``` ```
%% Cell type:code id:e644ab0e tags: %% Cell type:code id:e644ab0e tags:
``` python ``` python
y=y.astype('int') y=y.astype('int')
``` ```
%% Cell type:markdown id:6cee6462 tags: %% Cell type:markdown id:6cee6462 tags:
# Test train split # Test train split
%% Cell type:code id:1da48142 tags: %% Cell type:code id:1da48142 tags:
``` python ``` python
# split data into training and testing data-sets # split data into training and testing data-sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=7) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=7)
``` ```
%% Cell type:code id:129430e6 tags: %% Cell type:code id:129430e6 tags:
``` python ``` python
y_test.value_counts(),y_train.value_counts() y_test.value_counts(),y_train.value_counts()
``` ```
%% Output %% Output
(index (index
0 30 0 30
1 30 1 30
Name: count, dtype: int64, Name: count, dtype: int64,
index index
0 89 0 89
1 89 1 89
Name: count, dtype: int64) Name: count, dtype: int64)
%% Cell type:markdown id:1cfe2a06 tags: %% Cell type:markdown id:1cfe2a06 tags:
# Cross validation # Cross validation
%% Cell type:code id:d3550b5e tags: %% Cell type:code id:d3550b5e tags:
``` python ``` python
model = xgb.XGBClassifier(random_state=42) model = xgb.XGBClassifier(random_state=42)
# Defining parameter range # Defining parameter range
param_grid = { param_grid = {
'max_depth': [3,5], 'max_depth': [3,5],
'learning_rate': [0.1 ,0.01, 0.001], 'learning_rate': [0.1 ,0.01, 0.001],
'n_estimators': [100,200], 'n_estimators': [100,200],
'gamma': [ 0.1,0.01,0.001], 'gamma': [ 0.1,0.01,0.001],
'subsample': [1.0] 'subsample': [1.0]
} }
grid = GridSearchCV(model, param_grid, refit=True, verbose=3) grid = GridSearchCV(model, param_grid, refit=True, verbose=3)
# Fitting the model for grid search # Fitting the model for grid search
grid.fit(X_train, y_train) grid.fit(X_train, y_train)
``` ```
%% Output %% Output
Fitting 5 folds for each of 36 candidates, totalling 180 fits Fitting 5 folds for each of 36 candidates, totalling 180 fits
[CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.2s [CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.2s
[CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s [CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s
[CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s [CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s
[CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s [CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s
[CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 1/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 2/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 5/5] END gamma=0.1, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.4s [CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.4s
[CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s [CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s
[CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s [CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s
[CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s [CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s
[CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.1s [CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.1s
[CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.3s [CV 1/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.3s
[CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s [CV 2/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s
[CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s [CV 4/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s
[CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s [CV 5/5] END gamma=0.1, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s
[CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.1s [CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.1s
[CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s [CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s
[CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.1s [CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.1s
[CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s [CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s
[CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.3s [CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.3s
[CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s [CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s
[CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s [CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s
[CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s [CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s
[CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.1s [CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.1s
[CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s [CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s
[CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s [CV 1/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s
[CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.4s [CV 2/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.4s
[CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s [CV 3/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s
[CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s [CV 4/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s
[CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s [CV 5/5] END gamma=0.1, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s
[CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s [CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s
[CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 1/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 2/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 5/5] END gamma=0.01, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.917 total time= 0.2s [CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.917 total time= 0.2s
[CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.3s [CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.3s
[CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s [CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s
[CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s [CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s
[CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s [CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s
[CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s [CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s
[CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.917 total time= 0.2s [CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.917 total time= 0.2s
[CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.3s [CV 1/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.3s
[CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s [CV 2/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s
[CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s [CV 3/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s
[CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s [CV 4/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s
[CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s [CV 5/5] END gamma=0.01, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s
[CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.1s [CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.1s
[CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s [CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s
[CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.4s [CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.4s
[CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s [CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s
[CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s [CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s
[CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.1s [CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.1s
[CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.1s [CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.1s
[CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.3s [CV 1/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.3s
[CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.4s [CV 2/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.4s
[CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s [CV 3/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s
[CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s [CV 4/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s
[CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s [CV 5/5] END gamma=0.01, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s
[CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s [CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.1s
[CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.2s [CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=1.000 total time= 0.2s
[CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s [CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s
[CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s [CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.1s
[CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 1/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s [CV 2/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.2s
[CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s [CV 5/5] END gamma=0.001, learning_rate=0.1, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.2s
[CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.1s [CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.1s
[CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.4s [CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.4s
[CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s [CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.3s
[CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s [CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.3s
[CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s [CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s
[CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s [CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.1s
[CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.4s [CV 1/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=1.000 total time= 0.4s
[CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.4s [CV 2/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.917 total time= 0.4s
[CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s [CV 3/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s
[CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s [CV 4/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s
[CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s [CV 5/5] END gamma=0.001, learning_rate=0.01, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s
[CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.3s [CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.3s
[CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.3s [CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.3s
[CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s [CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.4s
[CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s [CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s
[CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s [CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=3, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.3s
[CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s [CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.944 total time= 0.2s
[CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s [CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.889 total time= 0.2s
[CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s [CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.972 total time= 0.2s
[CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s [CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.971 total time= 0.2s
[CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s [CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=100, subsample=1.0;, score=0.943 total time= 0.2s
[CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s [CV 1/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.944 total time= 0.4s
[CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.3s [CV 2/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.889 total time= 0.3s
[CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s [CV 3/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.972 total time= 0.3s
[CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s [CV 4/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.971 total time= 0.4s
[CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s [CV 5/5] END gamma=0.001, learning_rate=0.001, max_depth=5, n_estimators=200, subsample=1.0;, score=0.943 total time= 0.4s
GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None, GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,
callbacks=None, colsample_bylevel=None, callbacks=None, colsample_bylevel=None,
colsample_bynode=None, colsample_bynode=None,
colsample_bytree=None, colsample_bytree=None,
early_stopping_rounds=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, enable_categorical=False, eval_metric=None,
feature_types=None, gamma=None, feature_types=None, gamma=None,
gpu_id=None, grow_policy=None, gpu_id=None, grow_policy=None,
importance_type=None, importance_type=None,
interaction_constraints=None, interaction_constraints=None,
learning_rate=None, max_b... learning_rate=None, max_b...
max_cat_to_onehot=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=None, max_delta_step=None, max_depth=None,
max_leaves=None, min_child_weight=None, max_leaves=None, min_child_weight=None,
missing=nan, monotone_constraints=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None, n_estimators=100, n_jobs=None,
num_parallel_tree=None, predictor=None, num_parallel_tree=None, predictor=None,
random_state=42, ...), random_state=42, ...),
param_grid={'gamma': [0.1, 0.01, 0.001], param_grid={'gamma': [0.1, 0.01, 0.001],
'learning_rate': [0.1, 0.01, 0.001], 'learning_rate': [0.1, 0.01, 0.001],
'max_depth': [3, 5], 'n_estimators': [100, 200], 'max_depth': [3, 5], 'n_estimators': [100, 200],
'subsample': [1.0]}, 'subsample': [1.0]},
verbose=3) verbose=3)
%% Cell type:code id:556e249c tags: %% Cell type:code id:556e249c tags:
``` python ``` python
# print best parameter after tuning # print best parameter after tuning
print(grid.best_params_) print(grid.best_params_)
# print how our model looks after hyper-parameter tuning # print how our model looks after hyper-parameter tuning
print(grid.best_estimator_) print(grid.best_estimator_)
``` ```
%% Output %% Output
{'gamma': 0.1, 'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 1.0} {'gamma': 0.1, 'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100, 'subsample': 1.0}
XGBClassifier(base_score=None, booster=None, callbacks=None, XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None, colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None, colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None, enable_categorical=False, eval_metric=None, feature_types=None,
gamma=0.1, gpu_id=None, grow_policy=None, importance_type=None, gamma=0.1, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.1, max_bin=None, interaction_constraints=None, learning_rate=0.1, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None, max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=3, max_leaves=None, max_delta_step=None, max_depth=3, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None, min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None, num_parallel_tree=None, n_estimators=100, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=42, ...) predictor=None, random_state=42, ...)
%% Cell type:code id:0686e808 tags: %% Cell type:code id:0686e808 tags:
``` python ``` python
model_xgb = grid.best_estimator_ model_xgb = grid.best_estimator_
model_xgb.fit(X_train,y_train) model_xgb.fit(X_train,y_train)
``` ```
%% Output %% Output
XGBClassifier(base_score=None, booster=None, callbacks=None, XGBClassifier(base_score=None, booster=None, callbacks=None,
colsample_bylevel=None, colsample_bynode=None, colsample_bylevel=None, colsample_bynode=None,
colsample_bytree=None, early_stopping_rounds=None, colsample_bytree=None, early_stopping_rounds=None,
enable_categorical=False, eval_metric=None, feature_types=None, enable_categorical=False, eval_metric=None, feature_types=None,
gamma=0.1, gpu_id=None, grow_policy=None, importance_type=None, gamma=0.1, gpu_id=None, grow_policy=None, importance_type=None,
interaction_constraints=None, learning_rate=0.1, max_bin=None, interaction_constraints=None, learning_rate=0.1, max_bin=None,
max_cat_threshold=None, max_cat_to_onehot=None, max_cat_threshold=None, max_cat_to_onehot=None,
max_delta_step=None, max_depth=3, max_leaves=None, max_delta_step=None, max_depth=3, max_leaves=None,
min_child_weight=None, missing=nan, monotone_constraints=None, min_child_weight=None, missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None, num_parallel_tree=None, n_estimators=100, n_jobs=None, num_parallel_tree=None,
predictor=None, random_state=42, ...) predictor=None, random_state=42, ...)
%% Cell type:code id:ac776bef tags: %% Cell type:code id:ac776bef tags:
``` python ``` python
y_proba = model_xgb.fit(X_train, y_train).predict_proba(X_test) y_proba = model_xgb.fit(X_train, y_train).predict_proba(X_test)
``` ```
%% Cell type:markdown id:3ea57532 tags: %% Cell type:markdown id:3ea57532 tags:
# classification report # classification report
%% Cell type:code id:18becbe2 tags: %% Cell type:code id:18becbe2 tags:
``` python ``` python
from sklearn.metrics import classification_report, confusion_matrix from sklearn.metrics import classification_report, confusion_matrix
grid_predictions = grid.predict(X_test) grid_predictions = grid.predict(X_test)
print(classification_report(y_test, grid_predictions)) print(classification_report(y_test, grid_predictions))
``` ```
%% Output %% Output
precision recall f1-score support precision recall f1-score support
0 0.97 0.97 0.97 30 0 0.97 0.97 0.97 30
1 0.97 0.97 0.97 30 1 0.97 0.97 0.97 30
accuracy 0.97 60 accuracy 0.97 60
macro avg 0.97 0.97 0.97 60 macro avg 0.97 0.97 0.97 60
weighted avg 0.97 0.97 0.97 60 weighted avg 0.97 0.97 0.97 60
%% Cell type:code id:c0193b78 tags: %% Cell type:code id:c0193b78 tags:
``` python ``` python
classes = model_xgb.classes_ classes = model_xgb.classes_
``` ```
%% Cell type:code id:d723c69f tags: %% Cell type:code id:d723c69f tags:
``` python ``` python
classes classes
``` ```
%% Output %% Output
array([0, 1]) array([0, 1])
%% Cell type:code id:4643393d tags: %% Cell type:code id:4643393d tags:
``` python ``` python
#######CONFUSION MATRIX ########### #######CONFUSION MATRIX ###########
from sklearn import metrics from sklearn import metrics
y_test_pred_xgb = model_xgb.predict(X_test) y_test_pred_xgb = model_xgb.predict(X_test)
confusion_matrix_test = metrics.confusion_matrix(y_test, y_test_pred_xgb) confusion_matrix_test = metrics.confusion_matrix(y_test, y_test_pred_xgb)
cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix_test) cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix_test)
cm_display.plot() cm_display.plot()
plt.show() plt.show()
``` ```
%% Output %% Output
%% Cell type:code id:5ad4efb1 tags: %% Cell type:code id:5ad4efb1 tags:
``` python ``` python
total1=sum(sum(confusion_matrix_test)) total1=sum(sum(confusion_matrix_test))
#####from confusion matrix calculate accuracy #####from confusion matrix calculate accuracy
accuracy1=(confusion_matrix_test[0,0]+confusion_matrix_test[1,1])/total1 accuracy1=(confusion_matrix_test[0,0]+confusion_matrix_test[1,1])/total1
print ('Accuracy : ', accuracy1) print ('Accuracy : ', accuracy1)
sensitivity1 = confusion_matrix_test[0,0]/(confusion_matrix_test[0,0]+confusion_matrix_test[0,1]) sensitivity1 = confusion_matrix_test[0,0]/(confusion_matrix_test[0,0]+confusion_matrix_test[0,1])
print('Sensitivity : ', sensitivity1 ) print('Sensitivity : ', sensitivity1 )
specificity1 = confusion_matrix_test[1,1]/(confusion_matrix_test[1,0]+confusion_matrix_test[1,1]) specificity1 = confusion_matrix_test[1,1]/(confusion_matrix_test[1,0]+confusion_matrix_test[1,1])
print('Specificity : ', specificity1) print('Specificity : ', specificity1)
``` ```
%% Output %% Output
Accuracy : 0.9666666666666667 Accuracy : 0.9666666666666667
Sensitivity : 0.9666666666666667 Sensitivity : 0.9666666666666667
Specificity : 0.9666666666666667 Specificity : 0.9666666666666667
%% Cell type:markdown id:6603d82c tags: %% Cell type:markdown id:6603d82c tags:
# ROC curve # ROC curve
%% Cell type:code id:0e2a2694 tags: %% Cell type:code id:0e2a2694 tags:
``` python ``` python
from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import SelectKBest, f_classif from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.metrics import auc from sklearn.metrics import auc
def roc(X_train,y_train,model,label): def roc(X_train,y_train,model,label):
cv = StratifiedKFold(n_splits=6) cv = StratifiedKFold(n_splits=6)
classifier = model classifier = model
tprs = [] tprs = []
aucs = [] aucs = []
mean_fpr = np.linspace(0, 1, 100) mean_fpr = np.linspace(0, 1, 100)
fig, ax = plt.subplots(figsize=(6, 6)) fig, ax = plt.subplots(figsize=(6, 6))
for fold, (train, test) in enumerate(cv.split(X_train, y_train)): for fold, (train, test) in enumerate(cv.split(X_train, y_train)):
classifier.fit(X_train.iloc[train], y_train.iloc[train]) classifier.fit(X_train.iloc[train], y_train.iloc[train])
viz = RocCurveDisplay.from_estimator( viz = RocCurveDisplay.from_estimator(
classifier, classifier,
X_train.iloc[test], X_train.iloc[test],
y_train.iloc[test], y_train.iloc[test],
name=f"ROC fold {fold}", name=f"ROC fold {fold}",
alpha=0.3, alpha=0.3,
lw=1, lw=1,
ax=ax, ax=ax,
) )
interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr) interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
interp_tpr[0] = 0.0 interp_tpr[0] = 0.0
tprs.append(interp_tpr) tprs.append(interp_tpr)
aucs.append(viz.roc_auc) aucs.append(viz.roc_auc)
ax.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)") ax.plot([0, 1], [0, 1], "k--", label="chance level (AUC = 0.5)")
mean_tpr = np.mean(tprs, axis=0) mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0 mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr) mean_auc = auc(mean_fpr, mean_tpr)
std_auc = np.std(aucs) std_auc = np.std(aucs)
ax.plot( ax.plot(
mean_fpr, mean_fpr,
mean_tpr, mean_tpr,
color="b", color="b",
label=r"Mean ROC (AUC = %0.2f $\pm$ %0.2f)" % (mean_auc, std_auc), label=r"Mean ROC (AUC = %0.2f $\pm$ %0.2f)" % (mean_auc, std_auc),
lw=2, lw=2,
alpha=0.8, alpha=0.8,
) )
std_tpr = np.std(tprs, axis=0) std_tpr = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tpr + std_tpr, 1) tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
tprs_lower = np.maximum(mean_tpr - std_tpr, 0) tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
ax.fill_between( ax.fill_between(
mean_fpr, mean_fpr,
tprs_lower, tprs_lower,
tprs_upper, tprs_upper,
color="grey", color="grey",
alpha=0.2, alpha=0.2,
label=r"$\pm$ 1 std. dev.", label=r"$\pm$ 1 std. dev.",
) )
ax.set( ax.set(
xlim=[-0.05, 1.05], xlim=[-0.05, 1.05],
ylim=[-0.05, 1.05], ylim=[-0.05, 1.05],
xlabel="False Positive Rate", xlabel="False Positive Rate",
ylabel="True Positive Rate", ylabel="True Positive Rate",
title=label, title=label,
) )
ax.axis("square") ax.axis("square")
ax.legend(loc="lower right") ax.legend(loc="lower right")
plt.show() plt.show()
``` ```
%% Cell type:code id:d4cc8e6d tags: %% Cell type:code id:d4cc8e6d tags:
``` python ``` python
model = model_xgb model = model_xgb
label="ROC curve of training data" label="ROC curve of training data"
roc(X_train,y_train,model,label) roc(X_train,y_train,model,label)
``` ```
%% Output %% Output
%% Cell type:code id:1199e2e4 tags: %% Cell type:code id:1199e2e4 tags:
``` python ``` python
label="ROC curve of testing data" label="ROC curve of testing data"
roc(X_test,y_test,model,label) roc(X_test,y_test,model,label)
``` ```
%% Output %% Output
%% Cell type:markdown id:bee03388 tags: %% Cell type:markdown id:bee03388 tags:
# Feature importance # Feature importance
%% Cell type:code id:6688e037 tags: %% Cell type:code id:6688e037 tags:
``` python ``` python
# for important features: # for important features:
important_feat = model_xgb.feature_importances_ important_feat = model_xgb.feature_importances_
#get indices of those important features #get indices of those important features
idx = important_feat.argsort(kind= "quicksort") idx = important_feat.argsort(kind= "quicksort")
idx= idx[::-1][:50] idx= idx[::-1][:50]
``` ```
%% Cell type:code id:4e6a7ea1 tags: %% Cell type:code id:4e6a7ea1 tags:
``` python ``` python
idx idx
``` ```
%% Output %% Output
array([ 66, 65, 84, 94, 140, 32, 169, 137, 23, 212, 10, 166, 13, array([ 66, 65, 84, 94, 140, 32, 169, 137, 23, 212, 10, 166, 13,
36, 56, 126, 48, 57, 42, 208, 37, 113, 29, 160, 22, 96, 36, 56, 126, 48, 57, 42, 208, 37, 113, 29, 160, 22, 96,
162, 229, 189, 101, 104, 127, 135, 21, 79, 78, 77, 76, 75, 162, 229, 189, 101, 104, 127, 135, 21, 79, 78, 77, 76, 75,
74, 73, 72, 202, 71, 69, 68, 67, 64, 63, 62]) 74, 73, 72, 202, 71, 69, 68, 67, 64, 63, 62])
%% Cell type:code id:f2101fe1 tags: %% Cell type:code id:f2101fe1 tags:
``` python ``` python
df1 = X.T df1 = X.T
``` ```
%% Cell type:code id:2cbf1166 tags: %% Cell type:code id:2cbf1166 tags:
``` python ``` python
top_met = df1.iloc[idx] top_met = df1.iloc[idx]
``` ```
%% Cell type:code id:2370b2df tags: %% Cell type:code id:2370b2df tags:
``` python ``` python
top_met.index top_met.index
``` ```
%% Output %% Output
Index(['hsa-miR-18b-5p', 'hsa-miR-18a-5p', 'hsa-miR-21-5p', 'hsa-miR-25-3p', Index(['hsa-miR-18b-5p', 'hsa-miR-18a-5p', 'hsa-miR-21-5p', 'hsa-miR-25-3p',
'hsa-miR-424-5p', 'hsa-miR-130b-3p', 'hsa-miR-455-3p', 'hsa-miR-378i', 'hsa-miR-424-5p', 'hsa-miR-130b-3p', 'hsa-miR-455-3p', 'hsa-miR-378i',
'hsa-miR-1268a', 'hsa-miR-93-5p', 'hsa-miR-106b-5p', 'hsa-miR-451a', 'hsa-miR-1268a', 'hsa-miR-93-5p', 'hsa-miR-106b-5p', 'hsa-miR-451a',
'hsa-miR-10b-5p', 'hsa-miR-140-3p', 'hsa-miR-15b-5p', 'hsa-miR-3651', 'hsa-miR-10b-5p', 'hsa-miR-140-3p', 'hsa-miR-15b-5p', 'hsa-miR-3651',
'hsa-miR-150-5p', 'hsa-miR-16-2-3p', 'hsa-miR-145-5p', 'hsa-miR-7-5p', 'hsa-miR-150-5p', 'hsa-miR-16-2-3p', 'hsa-miR-145-5p', 'hsa-miR-7-5p',
'hsa-miR-140-5p', 'hsa-miR-3198', 'hsa-miR-1290', 'hsa-miR-4465', 'hsa-miR-140-5p', 'hsa-miR-3198', 'hsa-miR-1290', 'hsa-miR-4465',
'hsa-miR-126-3p', 'hsa-miR-26b-5p', 'hsa-miR-4497', 'mr_1', 'hsa-miR-126-3p', 'hsa-miR-26b-5p', 'hsa-miR-4497', 'mr_1',
'hsa-miR-497-5p', 'hsa-miR-29c-3p', 'hsa-miR-30a-5p', 'hsa-miR-3656', 'hsa-miR-497-5p', 'hsa-miR-29c-3p', 'hsa-miR-30a-5p', 'hsa-miR-3656',
'hsa-miR-378a-3p', 'hsa-miR-125b-5p', 'hsa-miR-200c-3p', 'hsa-miR-378a-3p', 'hsa-miR-125b-5p', 'hsa-miR-200c-3p',
'hsa-miR-200b-3p', 'hsa-miR-19b-3p', 'hsa-miR-19a-3p', 'hsa-miR-200b-3p', 'hsa-miR-19b-3p', 'hsa-miR-19a-3p',
'hsa-miR-199a-5p', 'hsa-miR-199a-3p', 'hsa-miR-1973', 'hsa-miR-197-5p', 'hsa-miR-199a-5p', 'hsa-miR-199a-3p', 'hsa-miR-1973', 'hsa-miR-197-5p',
'hsa-miR-642a-3p', 'hsa-miR-197-3p', 'hsa-miR-193b-3p', 'hsa-miR-642a-3p', 'hsa-miR-197-3p', 'hsa-miR-193b-3p',
'hsa-miR-193a-5p', 'hsa-miR-1915-3p', 'hsa-miR-188-5p', 'hsa-miR-193a-5p', 'hsa-miR-1915-3p', 'hsa-miR-188-5p',
'hsa-miR-185-5p', 'hsa-miR-181b-5p'], 'hsa-miR-185-5p', 'hsa-miR-181b-5p'],
dtype='object') dtype='object')
......
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