import pandas as pd
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import seaborn
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
import itertools
from sklearn.preprocessing import StandardScaler,MinMaxScaler
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib

galaxies = pd.read_csv('./LAMOST_spectra/galaxy_standardised.csv')

qsos = pd.read_csv('./LAMOST_spectra/qso_standardised.csv')

spectras = galaxies.append(qsos)
spectras

	0	1	2	3	4	5	6	7	8	9	...	2541	2542	2543	2544	2545	2546	2547	2548	2549	type
0	0.617188	1.078200	0.719656	0.591135	0.249724	0.576590	0.758004	0.434611	1.019012	0.431297	...	1.230564	2.066841	1.630086	0.345478	1.257950	-0.641574	0.866615	0.845419	0.875861	2
1	-0.105619	-0.615157	-0.151418	-0.192431	0.600306	0.485535	-0.460267	0.562241	0.145672	0.118304	...	0.952100	1.041957	0.870311	1.209000	1.210865	0.831403	0.682861	1.007102	0.998151	2
2	0.608529	0.339985	0.612112	0.704888	0.435795	0.564308	0.521174	0.646109	0.386403	0.227294	...	1.038180	1.026206	0.939450	1.023971	1.122343	0.985136	1.022401	1.032069	1.035573	2
3	-0.125019	1.094082	0.536864	1.023426	2.261266	2.303743	0.980048	0.621883	0.701422	0.720067	...	0.921314	0.763278	0.740817	1.109447	0.645768	0.984002	0.852604	0.803293	0.816611	2
4	0.646874	0.441916	0.839619	0.434925	0.544952	0.256422	0.467634	0.512585	0.236020	0.337063	...	1.184212	1.159360	0.886024	1.422036	1.451235	1.154935	1.108076	1.149500	1.149634	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1995	1.358673	2.255526	-1.927181	2.189852	-1.175295	3.158015	1.817735	1.476316	-0.005982	2.614979	...	14.729002	14.190329	6.227650	-6.466226	-6.466477	-6.980642	0.129756	0.899375	2.892127	3
1996	4.736062	8.493721	4.307473	6.376364	6.063689	4.828099	7.034339	5.206685	1.766484	8.031152	...	-2.224697	-2.945363	-3.193636	1.916528	-4.198533	-6.026858	-2.577864	-1.554188	-2.068489	3
1997	2.251206	3.835492	2.401364	2.064844	1.606885	2.835680	2.543361	3.106267	3.807246	3.216638	...	0.240475	0.018851	-0.472121	-0.058201	-0.391158	0.612688	0.288732	0.512242	0.547362	3
1998	1.734387	2.007688	1.798588	1.681123	2.155894	2.156851	1.905678	1.978067	2.447229	2.044550	...	0.460327	0.609559	0.467800	0.139381	-0.047443	0.301798	0.392577	0.467889	0.384213	3
1999	2.673625	2.610178	4.171594	1.135168	1.557421	3.336255	1.778019	2.815661	3.667999	2.136348	...	0.758155	-1.198771	-1.827310	-2.254883	2.136604	-1.001851	0.965404	0.122785	0.576359	3

4000 rows × 2551 columns

spectras= shuffle(spectras)
spectras

	0	1	2	3	4	5	6	7	8	9	...	2541	2542	2543	2544	2545	2546	2547	2548	2549	type
1224	2.891349	1.437608	1.346257	1.553989	2.882983	2.260688	0.524664	1.037580	0.385678	2.177028	...	1.114744	1.451957	-5.536831	0.532371	8.654653	-0.076086	1.326490	0.453447	0.754747	3
1830	3.879734	5.009363	6.276478	5.035055	5.840371	4.358024	5.324929	4.972554	2.555320	2.953080	...	0.675787	0.591280	1.069435	0.217655	0.115893	0.581067	0.514607	0.462505	0.649936	3
964	0.425303	-0.773273	1.868011	0.401713	2.898418	2.085099	1.737992	1.282685	1.108372	1.779551	...	0.595909	0.576578	0.345612	0.631520	0.907204	-0.226944	-0.068086	0.521514	0.633895	3
778	1.276239	4.518811	2.398222	2.322696	-0.104984	1.534739	3.207106	2.447320	2.416217	3.429941	...	0.434583	0.189397	-0.833621	-1.580476	-0.394436	0.575719	0.741413	0.076439	0.215710	3
112	3.604190	-0.166099	0.796164	3.779741	2.862189	2.297985	1.111864	-4.363558	3.344258	-1.236573	...	1.787055	0.912069	1.118230	-0.070411	-0.277597	0.754682	0.061647	0.575771	0.907901	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1812	0.093733	0.183721	0.050577	0.045006	-0.053974	0.103254	0.218459	0.296510	0.321800	0.262396	...	1.072321	1.172215	0.907360	1.556413	0.817625	0.919094	1.151508	1.290080	1.249178	2
532	0.868472	1.438901	1.794490	0.754563	0.680179	0.701802	0.556087	1.235091	0.780536	1.650703	...	0.847327	0.835260	0.950760	0.823709	0.704693	0.701405	0.901184	0.883823	0.856567	2
1813	2.330337	1.271180	2.015320	2.478237	1.989256	1.652456	1.838835	2.860579	3.167940	2.157424	...	0.198369	0.288275	0.102458	0.628728	0.734389	0.408767	0.310602	0.435959	0.471247	3
463	2.992803	4.520074	1.281727	2.560910	0.090832	1.549194	1.371671	-1.630202	-4.321544	-0.210890	...	-0.179296	-3.015679	-2.220191	-2.508168	0.847879	0.454679	-0.945904	-0.030512	0.630526	3
1915	0.275238	1.066475	0.346207	0.242615	0.915270	0.539574	0.767515	0.643072	0.644822	0.726666	...	0.827198	1.093432	1.058417	0.406138	0.578854	0.656705	0.672236	0.712788	0.703433	2

4000 rows × 2551 columns

X_train, X_test, y_train, y_test = train_test_split(spectras.iloc[:,0:2550],spectras['type'], test_size=0.2, random_state=0)

X_train, X_test, y_train, y_test = train_test_split(spectras.iloc[:,0:2550],spectras['type'], test_size=0.2)

X_train

	0	1	2	3	4	5	6	7	8	9	...	2540	2541	2542	2543	2544	2545	2546	2547	2548	2549
496	0.453018	-0.078946	1.056244	-0.761460	-0.108049	0.316049	1.684295	1.277893	2.328646	-0.490003	...	1.023142	0.972128	0.877626	0.903512	0.225082	0.656252	1.250650	0.905113	1.123623	1.008364
1241	0.744684	0.706419	0.539418	0.585637	1.159545	0.676108	0.581048	0.410185	0.660224	0.516061	...	0.728807	0.872218	1.278320	0.577979	0.967569	0.949157	0.762882	0.773693	0.721741	0.852410
1864	0.549835	0.339083	1.420260	0.710170	1.010419	0.306943	-0.252886	0.437769	1.991301	0.414019	...	0.846816	3.304601	15.221448	-6.067144	-1.988856	6.030686	2.592641	1.119804	0.687274	0.704317
571	-7.031517	6.187820	-9.371308	8.060097	-3.906795	2.382266	-0.073769	6.968823	2.481344	8.414966	...	1.881450	-0.323327	2.411483	-3.733856	1.817483	-1.802364	3.208507	1.380639	1.492592	0.997739
1094	0.539701	0.670896	-0.184938	0.392194	0.096106	0.525495	0.418713	0.776471	0.828761	0.931785	...	0.894532	0.855367	0.714974	0.750959	0.940317	0.995974	1.013581	0.925367	0.879055	0.889729
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1378	2.394843	1.695996	1.064526	2.684924	1.394738	3.577144	3.067955	0.946760	2.945100	1.817511	...	1.051854	3.754546	8.477930	1.168571	-6.302239	1.473663	-0.638889	0.444808	0.817370	1.119679
478	1.302975	4.023532	0.029693	3.330072	1.782232	1.015794	2.910030	2.706038	3.512573	1.349334	...	0.693626	0.542258	2.424630	-0.655173	0.647713	1.220641	1.382282	0.817242	0.609625	0.833427
1581	0.259939	0.039702	0.313820	0.330721	0.457388	0.079465	0.333012	0.251914	0.294523	-0.090406	...	1.411705	1.464086	1.367845	1.287620	1.276434	1.035445	1.370057	1.555483	1.340720	1.450895
438	1.505315	1.813736	1.585130	1.981320	1.795147	1.644358	1.660030	1.656898	1.302048	1.976754	...	0.591836	1.096665	2.010072	0.355882	-0.075982	0.229903	0.325398	0.396209	0.228675	0.527300
409	1.552886	0.375319	1.453693	2.159667	1.925261	0.457191	0.414648	1.671543	2.754634	0.379460	...	0.461849	0.226023	-0.802757	0.957317	2.737699	1.378962	-0.427378	-0.353147	0.238610	0.332487

3200 rows × 2550 columns

y_train

496     2
1241    2
1864    3
571     2
1094    2
       ..
1378    3
478     3
1581    2
438     3
409     3
Name: type, Length: 3200, dtype: int64

scaler = MinMaxScaler().fit(X_train)

X_train_minmax = scaler.transform(X_train)
X_test_minmax = scaler.transform(X_test)

scaler = StandardScaler().fit(X_train_minmax)

X_train_std = scaler.transform(X_train_minmax)
X_test_std = scaler.transform(X_test_minmax)

pca = PCA(n_components=3)
pca.fit(X_train_std)
X_train_pca = pca.transform(X_train_std)
X_test_pca = pca.transform(X_test_std)

X_train_pca

array([[-1.22708853, -1.97466713,  0.96322612],
       [-1.08824241, -1.98833355,  1.31729912],
       [-1.48126539, -0.86763153,  0.47860654],
       ...,
       [-2.98066801, -8.43210338,  5.83999212],
       [ 0.10952244,  2.71689867, -2.4370318 ],
       [ 0.01992795,  2.80675104, -2.50848414]])

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(X_train_pca[:,0],X_train_pca[:,1],X_train_pca[:,2],c=y_train,alpha=0.5)
ax.set_xlabel('component 1')
ax.set_ylabel('component 2')
ax.set_zlabel('component 3')
plt.show()

# ax = plt.figure()
# plt.scatter(X_train_pca[:,0], X_train_pca[:,1],c=y_train,alpha=0.5)
# plt.xlabel('Component 1')
# plt.ylabel('Component 2')
# plt.title('Spectra after PCA(components=2)')
# plt.show()

pca=PCA().fit(X_train_std)
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance');
plt.show()

pca = PCA(n_components=500)
pca.fit(X_train)
X_train_pca = pca.transform(X_train)
X_train_pca

array([[-3.71978188e+01, -1.41418877e+00, -1.89891264e+01, ...,
        -8.30188298e-01, -4.14604081e-01, -5.31432096e-01],
       [-3.88501440e+01,  1.14735320e+00, -1.46524801e+01, ...,
         1.62483877e-01,  1.46106846e-01, -1.61429009e-01],
       [-3.91866403e+01, -1.09620587e+00, -6.92791259e+00, ...,
        -1.49211888e-02, -1.24995228e-01, -7.50978608e-02],
       ...,
       [-3.95706639e+01, -2.83502962e+00, -2.47977053e+00, ...,
         2.75424368e-01,  6.35256314e-02, -1.76100910e-01],
       [-4.11939045e+01,  8.16427298e-01,  7.61207222e+00, ...,
        -1.02201175e-01,  3.18930861e-01, -7.96342761e-02],
       [-4.11512791e+01, -1.79628578e+00,  1.03680603e+01, ...,
         2.07216166e-01,  2.20259600e-02, -3.75345051e-01]])

RandomForestClassifier?

rf = RandomForestClassifier(oob_score=True)

rf.fit(X_train,y_train)

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=True, random_state=None,
                       verbose=0, warm_start=False)

y_predict_rf = rf.predict(X_test)

y_predict_rf

array([2, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, 2, 2, 2, 3, 3, 2, 2, 3, 2,
       2, 2, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 3,
       3, 2, 3, 3, 2, 3, 3, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3, 3, 2,
       3, 3, 2, 2, 3, 2, 2, 3, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 2, 3, 2, 2,
       3, 3, 3, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 3, 3, 2, 3, 2,
       3, 2, 2, 2, 2, 3, 3, 2, 3, 2, 2, 3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 2,
       2, 2, 3, 3, 2, 2, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3,
       3, 2, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 3, 3, 2, 3, 2, 2, 2,
       3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2,
       2, 3, 3, 3, 3, 3, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2, 2,
       3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3,
       3, 2, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,
       2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2, 3, 2, 3, 2, 3, 2, 2, 2, 3, 2, 2,
       3, 3, 2, 2, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 2, 2,
       3, 3, 2, 2, 3, 2, 3, 3, 3, 2, 2, 3, 2, 2, 3, 2, 3, 3, 3, 3, 2, 3,
       3, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 3,
       2, 3, 3, 2, 2, 2, 3, 2, 2, 3, 3, 3, 2, 2, 2, 3, 3, 3, 2, 3, 2, 3,
       3, 3, 3, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 3,
       2, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 3, 2, 3, 2, 3,
       2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 2, 3, 2, 2, 3, 3,
       3, 3, 2, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 2,
       3, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 2, 3, 2, 3,
       2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 3, 3, 3, 3,
       2, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 3,
       2, 3, 2, 2, 3, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 2, 2, 2, 3, 2, 2,
       3, 2, 2, 2, 2, 3, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 3, 2, 2, 2, 2,
       3, 3, 2, 2, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 2, 2,
       2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 2, 3, 2,
       3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2,
       2, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 3, 3, 2, 3,
       2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 3, 2, 3,
       2, 3, 3, 2, 3, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 2, 2, 2, 3,
       3, 3, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 2,
       3, 3, 3, 3, 2, 2, 2, 3, 3, 2, 2, 3, 3, 3, 3, 2, 2, 3, 3, 2, 2, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 2, 3, 3,
       3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3,
       3, 3, 3, 3, 3, 3, 2, 3])

y_predict_proba_rf = rf.predict_proba(X_test)
y_predict_proba_rf

array([[1.  , 0.  ],
       [0.11, 0.89],
       [0.04, 0.96],
       ...,
       [0.1 , 0.9 ],
       [1.  , 0.  ],
       [0.  , 1.  ]])

cnf_matrix = metrics.confusion_matrix(y_test,y_predict_rf)
cnf_matrix

array([[363,  55],
       [ 26, 356]])

class_names = ['galaxy','qso']
metrics.plot_confusion_matrix(rf, X_test, y_test,display_labels=class_names,
                                 cmap=plt.cm.Blues,values_format='g')

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fd6a9a15710>

print(metrics.classification_report(y_test,y_predict_rf,target_names=['galaxy','qso']))

              precision    recall  f1-score   support

      galaxy       0.94      0.87      0.91       418
         qso       0.87      0.94      0.90       382

    accuracy                           0.91       800
   macro avg       0.91      0.91      0.90       800
weighted avg       0.91      0.91      0.91       800

metrics.roc_auc_score(y_test, y_predict_rf)

0.9066108870462686

rf.oob_score_

0.9071875

metrics.plot_roc_curve(rf, X_test, y_test)

<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x7fd6a88d76a0>

importance = rf.feature_importances_

for i,v in enumerate(importance):
    print('Feature: %0d, Score: %.5f' % (i,v))

Feature: 0, Score: 0.00017
Feature: 1, Score: 0.00009
Feature: 2, Score: 0.00055
Feature: 3, Score: 0.00013
Feature: 4, Score: 0.00002
Feature: 5, Score: 0.00026
Feature: 6, Score: 0.00070
Feature: 7, Score: 0.00040
Feature: 8, Score: 0.00015
Feature: 9, Score: 0.00018
Feature: 10, Score: 0.00061
Feature: 11, Score: 0.00007
Feature: 12, Score: 0.00066
Feature: 13, Score: 0.00054
Feature: 14, Score: 0.00028
Feature: 15, Score: 0.00020
Feature: 16, Score: 0.00136
Feature: 17, Score: 0.00023
Feature: 18, Score: 0.00053
Feature: 19, Score: 0.00039
Feature: 20, Score: 0.00069
Feature: 21, Score: 0.00135
Feature: 22, Score: 0.00082
Feature: 23, Score: 0.00080
Feature: 24, Score: 0.00022
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Feature: 1969, Score: 0.00008
Feature: 1970, Score: 0.00002
Feature: 1971, Score: 0.00003
Feature: 1972, Score: 0.00007
Feature: 1973, Score: 0.00002
Feature: 1974, Score: 0.00001
Feature: 1975, Score: 0.00001
Feature: 1976, Score: 0.00009
Feature: 1977, Score: 0.00010
Feature: 1978, Score: 0.00002
Feature: 1979, Score: 0.00010
Feature: 1980, Score: 0.00007
Feature: 1981, Score: 0.00020
Feature: 1982, Score: 0.00014
Feature: 1983, Score: 0.00010
Feature: 1984, Score: 0.00024
Feature: 1985, Score: 0.00009
Feature: 1986, Score: 0.00006
Feature: 1987, Score: 0.00011
Feature: 1988, Score: 0.00005
Feature: 1989, Score: 0.00022
Feature: 1990, Score: 0.00019
Feature: 1991, Score: 0.00012
Feature: 1992, Score: 0.00011
Feature: 1993, Score: 0.00004
Feature: 1994, Score: 0.00479
Feature: 1995, Score: 0.00541
Feature: 1996, Score: 0.00005
Feature: 1997, Score: 0.00014
Feature: 1998, Score: 0.00010
Feature: 1999, Score: 0.00019
Feature: 2000, Score: 0.00010
Feature: 2001, Score: 0.00012
Feature: 2002, Score: 0.00002
Feature: 2003, Score: 0.00003
Feature: 2004, Score: 0.00001
Feature: 2005, Score: 0.00011
Feature: 2006, Score: 0.00008
Feature: 2007, Score: 0.00005
Feature: 2008, Score: 0.00175
Feature: 2009, Score: 0.00001
Feature: 2010, Score: 0.00011
Feature: 2011, Score: 0.00009
Feature: 2012, Score: 0.00018
Feature: 2013, Score: 0.00010
Feature: 2014, Score: 0.00006
Feature: 2015, Score: 0.00008
Feature: 2016, Score: 0.00015
Feature: 2017, Score: 0.00004
Feature: 2018, Score: 0.00013
Feature: 2019, Score: 0.00012
Feature: 2020, Score: 0.00001
Feature: 2021, Score: 0.00005
Feature: 2022, Score: 0.00002
Feature: 2023, Score: 0.00022
Feature: 2024, Score: 0.00550
Feature: 2025, Score: 0.00019
Feature: 2026, Score: 0.00016
Feature: 2027, Score: 0.00015
Feature: 2028, Score: 0.00018
Feature: 2029, Score: 0.00011
Feature: 2030, Score: 0.00012
Feature: 2031, Score: 0.00002
Feature: 2032, Score: 0.00004
Feature: 2033, Score: 0.00017
Feature: 2034, Score: 0.00003
Feature: 2035, Score: 0.00017
Feature: 2036, Score: 0.00000
Feature: 2037, Score: 0.00002
Feature: 2038, Score: 0.00015
Feature: 2039, Score: 0.00001
Feature: 2040, Score: 0.00091
Feature: 2041, Score: 0.00025
Feature: 2042, Score: 0.00011
Feature: 2043, Score: 0.00039
Feature: 2044, Score: 0.00014
Feature: 2045, Score: 0.00028
Feature: 2046, Score: 0.00010
Feature: 2047, Score: 0.00006
Feature: 2048, Score: 0.00010
Feature: 2049, Score: 0.00006
Feature: 2050, Score: 0.00107
Feature: 2051, Score: 0.00007
Feature: 2052, Score: 0.01445
Feature: 2053, Score: 0.00462
Feature: 2054, Score: 0.00010
Feature: 2055, Score: 0.00494
Feature: 2056, Score: 0.00009
Feature: 2057, Score: 0.00003
Feature: 2058, Score: 0.00003
Feature: 2059, Score: 0.00002
Feature: 2060, Score: 0.00003
Feature: 2061, Score: 0.00010
Feature: 2062, Score: 0.00006
Feature: 2063, Score: 0.00010
Feature: 2064, Score: 0.00012
Feature: 2065, Score: 0.00004
Feature: 2066, Score: 0.00027
Feature: 2067, Score: 0.00007
Feature: 2068, Score: 0.00011
Feature: 2069, Score: 0.00030
Feature: 2070, Score: 0.00003
Feature: 2071, Score: 0.00008
Feature: 2072, Score: 0.00006
Feature: 2073, Score: 0.00019
Feature: 2074, Score: 0.00006
Feature: 2075, Score: 0.00017
Feature: 2076, Score: 0.00014
Feature: 2077, Score: 0.00011
Feature: 2078, Score: 0.00006
Feature: 2079, Score: 0.00005
Feature: 2080, Score: 0.00017
Feature: 2081, Score: 0.00007
Feature: 2082, Score: 0.00011
Feature: 2083, Score: 0.00016
Feature: 2084, Score: 0.00004
Feature: 2085, Score: 0.00003
Feature: 2086, Score: 0.00004
Feature: 2087, Score: 0.00011
Feature: 2088, Score: 0.00004
Feature: 2089, Score: 0.00003
Feature: 2090, Score: 0.00007
Feature: 2091, Score: 0.00005
Feature: 2092, Score: 0.00006
Feature: 2093, Score: 0.00004
Feature: 2094, Score: 0.00001
Feature: 2095, Score: 0.00010
Feature: 2096, Score: 0.00005
Feature: 2097, Score: 0.00012
Feature: 2098, Score: 0.00000
Feature: 2099, Score: 0.00010
Feature: 2100, Score: 0.00008
Feature: 2101, Score: 0.00009
Feature: 2102, Score: 0.00019
Feature: 2103, Score: 0.00028
Feature: 2104, Score: 0.00538
Feature: 2105, Score: 0.01031
Feature: 2106, Score: 0.00508
Feature: 2107, Score: 0.00016
Feature: 2108, Score: 0.00009
Feature: 2109, Score: 0.00018
Feature: 2110, Score: 0.00013
Feature: 2111, Score: 0.00014
Feature: 2112, Score: 0.00004
Feature: 2113, Score: 0.00011
Feature: 2114, Score: 0.00003
Feature: 2115, Score: 0.00011
Feature: 2116, Score: 0.00014
Feature: 2117, Score: 0.00016
Feature: 2118, Score: 0.00027
Feature: 2119, Score: 0.00009
Feature: 2120, Score: 0.00009
Feature: 2121, Score: 0.00495
Feature: 2122, Score: 0.00024
Feature: 2123, Score: 0.00515
Feature: 2124, Score: 0.00507
Feature: 2125, Score: 0.00950
Feature: 2126, Score: 0.00009
Feature: 2127, Score: 0.00004
Feature: 2128, Score: 0.00014
Feature: 2129, Score: 0.00025
Feature: 2130, Score: 0.00501
Feature: 2131, Score: 0.00017
Feature: 2132, Score: 0.00015
Feature: 2133, Score: 0.00000
Feature: 2134, Score: 0.00021
Feature: 2135, Score: 0.00018
Feature: 2136, Score: 0.00536
Feature: 2137, Score: 0.00004
Feature: 2138, Score: 0.00009
Feature: 2139, Score: 0.00492
Feature: 2140, Score: 0.00012
Feature: 2141, Score: 0.00024
Feature: 2142, Score: 0.00049
Feature: 2143, Score: 0.00483
Feature: 2144, Score: 0.00013
Feature: 2145, Score: 0.01505
Feature: 2146, Score: 0.00553
Feature: 2147, Score: 0.00570
Feature: 2148, Score: 0.00540
Feature: 2149, Score: 0.01020
Feature: 2150, Score: 0.00573
Feature: 2151, Score: 0.00020
Feature: 2152, Score: 0.00019
Feature: 2153, Score: 0.00011
Feature: 2154, Score: 0.00007
Feature: 2155, Score: 0.00019
Feature: 2156, Score: 0.00022
Feature: 2157, Score: 0.00496
Feature: 2158, Score: 0.00005
Feature: 2159, Score: 0.00002
Feature: 2160, Score: 0.00487
Feature: 2161, Score: 0.00019
Feature: 2162, Score: 0.00009
Feature: 2163, Score: 0.00035
Feature: 2164, Score: 0.00003
Feature: 2165, Score: 0.00114
Feature: 2166, Score: 0.00003
Feature: 2167, Score: 0.00007
Feature: 2168, Score: 0.00016
Feature: 2169, Score: 0.00995
Feature: 2170, Score: 0.00018
Feature: 2171, Score: 0.00002
Feature: 2172, Score: 0.00472
Feature: 2173, Score: 0.00583
Feature: 2174, Score: 0.00008
Feature: 2175, Score: 0.00008
Feature: 2176, Score: 0.00010
Feature: 2177, Score: 0.00521
Feature: 2178, Score: 0.00989
Feature: 2179, Score: 0.00482
Feature: 2180, Score: 0.00024
Feature: 2181, Score: 0.00019
Feature: 2182, Score: 0.00000
Feature: 2183, Score: 0.00024
Feature: 2184, Score: 0.00002
Feature: 2185, Score: 0.00023
Feature: 2186, Score: 0.00004
Feature: 2187, Score: 0.00002
Feature: 2188, Score: 0.00447
Feature: 2189, Score: 0.00981
Feature: 2190, Score: 0.00488
Feature: 2191, Score: 0.00014
Feature: 2192, Score: 0.00004
Feature: 2193, Score: 0.00028
Feature: 2194, Score: 0.00032
Feature: 2195, Score: 0.00006
Feature: 2196, Score: 0.00015
Feature: 2197, Score: 0.00001
Feature: 2198, Score: 0.00006
Feature: 2199, Score: 0.00000
Feature: 2200, Score: 0.00010
Feature: 2201, Score: 0.00003
Feature: 2202, Score: 0.00997
Feature: 2203, Score: 0.00013
Feature: 2204, Score: 0.00001
Feature: 2205, Score: 0.00503
Feature: 2206, Score: 0.00497
Feature: 2207, Score: 0.00013
Feature: 2208, Score: 0.00013
Feature: 2209, Score: 0.00021
Feature: 2210, Score: 0.01486
Feature: 2211, Score: 0.00518
Feature: 2212, Score: 0.00018
Feature: 2213, Score: 0.00008
Feature: 2214, Score: 0.00042
Feature: 2215, Score: 0.00500
Feature: 2216, Score: 0.00473
Feature: 2217, Score: 0.00517
Feature: 2218, Score: 0.00498
Feature: 2219, Score: 0.01031
Feature: 2220, Score: 0.00008
Feature: 2221, Score: 0.04032
Feature: 2222, Score: 0.01065
Feature: 2223, Score: 0.00975
Feature: 2224, Score: 0.00499
Feature: 2225, Score: 0.00003
Feature: 2226, Score: 0.01023
Feature: 2227, Score: 0.00002
Feature: 2228, Score: 0.00021
Feature: 2229, Score: 0.00506
Feature: 2230, Score: 0.00009
Feature: 2231, Score: 0.00006
Feature: 2232, Score: 0.00007
Feature: 2233, Score: 0.00007
Feature: 2234, Score: 0.00002
Feature: 2235, Score: 0.00023
Feature: 2236, Score: 0.00007
Feature: 2237, Score: 0.00007
Feature: 2238, Score: 0.00000
Feature: 2239, Score: 0.00014
Feature: 2240, Score: 0.00007
Feature: 2241, Score: 0.00004
Feature: 2242, Score: 0.00002
Feature: 2243, Score: 0.00010
Feature: 2244, Score: 0.00005
Feature: 2245, Score: 0.00005
Feature: 2246, Score: 0.00019
Feature: 2247, Score: 0.00014
Feature: 2248, Score: 0.00009
Feature: 2249, Score: 0.00002
Feature: 2250, Score: 0.00004
Feature: 2251, Score: 0.00005
Feature: 2252, Score: 0.00018
Feature: 2253, Score: 0.00001
Feature: 2254, Score: 0.00008
Feature: 2255, Score: 0.00012
Feature: 2256, Score: 0.00005
Feature: 2257, Score: 0.00003
Feature: 2258, Score: 0.00002
Feature: 2259, Score: 0.00007
Feature: 2260, Score: 0.00012
Feature: 2261, Score: 0.00027
Feature: 2262, Score: 0.00032
Feature: 2263, Score: 0.00014
Feature: 2264, Score: 0.00991
Feature: 2265, Score: 0.01031
Feature: 2266, Score: 0.00036
Feature: 2267, Score: 0.00007
Feature: 2268, Score: 0.00038
Feature: 2269, Score: 0.00015
Feature: 2270, Score: 0.00026
Feature: 2271, Score: 0.00017
Feature: 2272, Score: 0.00008
Feature: 2273, Score: 0.00007
Feature: 2274, Score: 0.00012
Feature: 2275, Score: 0.00015
Feature: 2276, Score: 0.00009
Feature: 2277, Score: 0.00015
Feature: 2278, Score: 0.00004
Feature: 2279, Score: 0.00003
Feature: 2280, Score: 0.00010
Feature: 2281, Score: 0.00007
Feature: 2282, Score: 0.00009
Feature: 2283, Score: 0.00030
Feature: 2284, Score: 0.00009
Feature: 2285, Score: 0.00006
Feature: 2286, Score: 0.00562
Feature: 2287, Score: 0.00986
Feature: 2288, Score: 0.00514
Feature: 2289, Score: 0.00015
Feature: 2290, Score: 0.00010
Feature: 2291, Score: 0.00001
Feature: 2292, Score: 0.00025
Feature: 2293, Score: 0.00014
Feature: 2294, Score: 0.00005
Feature: 2295, Score: 0.00012
Feature: 2296, Score: 0.00001
Feature: 2297, Score: 0.00002
Feature: 2298, Score: 0.00020
Feature: 2299, Score: 0.00006
Feature: 2300, Score: 0.00011
Feature: 2301, Score: 0.00002
Feature: 2302, Score: 0.00011
Feature: 2303, Score: 0.00008
Feature: 2304, Score: 0.00014
Feature: 2305, Score: 0.00013
Feature: 2306, Score: 0.00008
Feature: 2307, Score: 0.00002
Feature: 2308, Score: 0.00000
Feature: 2309, Score: 0.00007
Feature: 2310, Score: 0.00017
Feature: 2311, Score: 0.00024
Feature: 2312, Score: 0.00016
Feature: 2313, Score: 0.00018
Feature: 2314, Score: 0.00013
Feature: 2315, Score: 0.00004
Feature: 2316, Score: 0.00011
Feature: 2317, Score: 0.00004
Feature: 2318, Score: 0.00011
Feature: 2319, Score: 0.00012
Feature: 2320, Score: 0.00106
Feature: 2321, Score: 0.00001
Feature: 2322, Score: 0.00002
Feature: 2323, Score: 0.00013
Feature: 2324, Score: 0.00020
Feature: 2325, Score: 0.00005
Feature: 2326, Score: 0.00010
Feature: 2327, Score: 0.00002
Feature: 2328, Score: 0.00011
Feature: 2329, Score: 0.00012
Feature: 2330, Score: 0.00008
Feature: 2331, Score: 0.00010
Feature: 2332, Score: 0.00003
Feature: 2333, Score: 0.00012
Feature: 2334, Score: 0.00009
Feature: 2335, Score: 0.00013
Feature: 2336, Score: 0.00014
Feature: 2337, Score: 0.00011
Feature: 2338, Score: 0.00007
Feature: 2339, Score: 0.00010
Feature: 2340, Score: 0.00001
Feature: 2341, Score: 0.00006
Feature: 2342, Score: 0.00002
Feature: 2343, Score: 0.00007
Feature: 2344, Score: 0.00020
Feature: 2345, Score: 0.00021
Feature: 2346, Score: 0.00010
Feature: 2347, Score: 0.00012
Feature: 2348, Score: 0.00011
Feature: 2349, Score: 0.00005
Feature: 2350, Score: 0.00001
Feature: 2351, Score: 0.00006
Feature: 2352, Score: 0.00007
Feature: 2353, Score: 0.00000
Feature: 2354, Score: 0.00002
Feature: 2355, Score: 0.00012
Feature: 2356, Score: 0.00001
Feature: 2357, Score: 0.00011
Feature: 2358, Score: 0.00003
Feature: 2359, Score: 0.00451
Feature: 2360, Score: 0.00501
Feature: 2361, Score: 0.00114
Feature: 2362, Score: 0.00004
Feature: 2363, Score: 0.00008
Feature: 2364, Score: 0.00007
Feature: 2365, Score: 0.00011
Feature: 2366, Score: 0.00001
Feature: 2367, Score: 0.00020
Feature: 2368, Score: 0.00003
Feature: 2369, Score: 0.00008
Feature: 2370, Score: 0.00000
Feature: 2371, Score: 0.00006
Feature: 2372, Score: 0.00001
Feature: 2373, Score: 0.00014
Feature: 2374, Score: 0.00009
Feature: 2375, Score: 0.00007
Feature: 2376, Score: 0.00016
Feature: 2377, Score: 0.00017
Feature: 2378, Score: 0.00004
Feature: 2379, Score: 0.00006
Feature: 2380, Score: 0.00474
Feature: 2381, Score: 0.00016
Feature: 2382, Score: 0.00049
Feature: 2383, Score: 0.00457
Feature: 2384, Score: 0.00121
Feature: 2385, Score: 0.00023
Feature: 2386, Score: 0.00008
Feature: 2387, Score: 0.00002
Feature: 2388, Score: 0.00014
Feature: 2389, Score: 0.00005
Feature: 2390, Score: 0.00002
Feature: 2391, Score: 0.00022
Feature: 2392, Score: 0.00017
Feature: 2393, Score: 0.00001
Feature: 2394, Score: 0.00001
Feature: 2395, Score: 0.00005
Feature: 2396, Score: 0.00003
Feature: 2397, Score: 0.00009
Feature: 2398, Score: 0.00006
Feature: 2399, Score: 0.00002
Feature: 2400, Score: 0.00009
Feature: 2401, Score: 0.00003
Feature: 2402, Score: 0.00010
Feature: 2403, Score: 0.00016
Feature: 2404, Score: 0.00085
Feature: 2405, Score: 0.00000
Feature: 2406, Score: 0.00007
Feature: 2407, Score: 0.00010
Feature: 2408, Score: 0.00010
Feature: 2409, Score: 0.00007
Feature: 2410, Score: 0.00015
Feature: 2411, Score: 0.00006
Feature: 2412, Score: 0.00000
Feature: 2413, Score: 0.00005
Feature: 2414, Score: 0.00011
Feature: 2415, Score: 0.00022
Feature: 2416, Score: 0.00022
Feature: 2417, Score: 0.00003
Feature: 2418, Score: 0.00009
Feature: 2419, Score: 0.00006
Feature: 2420, Score: 0.00028
Feature: 2421, Score: 0.00005
Feature: 2422, Score: 0.00009
Feature: 2423, Score: 0.00009
Feature: 2424, Score: 0.00010
Feature: 2425, Score: 0.00007
Feature: 2426, Score: 0.00002
Feature: 2427, Score: 0.00014
Feature: 2428, Score: 0.00010
Feature: 2429, Score: 0.00011
Feature: 2430, Score: 0.00006
Feature: 2431, Score: 0.00004
Feature: 2432, Score: 0.00018
Feature: 2433, Score: 0.00021
Feature: 2434, Score: 0.00007
Feature: 2435, Score: 0.00009
Feature: 2436, Score: 0.00003
Feature: 2437, Score: 0.00006
Feature: 2438, Score: 0.00000
Feature: 2439, Score: 0.00012
Feature: 2440, Score: 0.00006
Feature: 2441, Score: 0.00010
Feature: 2442, Score: 0.00008
Feature: 2443, Score: 0.00034
Feature: 2444, Score: 0.00003
Feature: 2445, Score: 0.00031
Feature: 2446, Score: 0.00008
Feature: 2447, Score: 0.00012
Feature: 2448, Score: 0.00012
Feature: 2449, Score: 0.00009
Feature: 2450, Score: 0.00007
Feature: 2451, Score: 0.00007
Feature: 2452, Score: 0.00026
Feature: 2453, Score: 0.00007
Feature: 2454, Score: 0.00007
Feature: 2455, Score: 0.00005
Feature: 2456, Score: 0.00014
Feature: 2457, Score: 0.00010
Feature: 2458, Score: 0.00005
Feature: 2459, Score: 0.00008
Feature: 2460, Score: 0.00008
Feature: 2461, Score: 0.00000
Feature: 2462, Score: 0.00006
Feature: 2463, Score: 0.00019
Feature: 2464, Score: 0.00002
Feature: 2465, Score: 0.00003
Feature: 2466, Score: 0.00025
Feature: 2467, Score: 0.00008
Feature: 2468, Score: 0.00012
Feature: 2469, Score: 0.00004
Feature: 2470, Score: 0.00007
Feature: 2471, Score: 0.00009
Feature: 2472, Score: 0.00004
Feature: 2473, Score: 0.00011
Feature: 2474, Score: 0.00004
Feature: 2475, Score: 0.00003
Feature: 2476, Score: 0.00000
Feature: 2477, Score: 0.00014
Feature: 2478, Score: 0.00004
Feature: 2479, Score: 0.00005
Feature: 2480, Score: 0.00004
Feature: 2481, Score: 0.00012
Feature: 2482, Score: 0.00023
Feature: 2483, Score: 0.00010
Feature: 2484, Score: 0.00009
Feature: 2485, Score: 0.00007
Feature: 2486, Score: 0.00002
Feature: 2487, Score: 0.00007
Feature: 2488, Score: 0.00004
Feature: 2489, Score: 0.00020
Feature: 2490, Score: 0.00007
Feature: 2491, Score: 0.00004
Feature: 2492, Score: 0.00001
Feature: 2493, Score: 0.00016
Feature: 2494, Score: 0.00003
Feature: 2495, Score: 0.00008
Feature: 2496, Score: 0.00004
Feature: 2497, Score: 0.00021
Feature: 2498, Score: 0.00009
Feature: 2499, Score: 0.00045
Feature: 2500, Score: 0.00493
Feature: 2501, Score: 0.00010
Feature: 2502, Score: 0.00005
Feature: 2503, Score: 0.00491
Feature: 2504, Score: 0.00013
Feature: 2505, Score: 0.00039
Feature: 2506, Score: 0.00004
Feature: 2507, Score: 0.00105
Feature: 2508, Score: 0.00017
Feature: 2509, Score: 0.00110
Feature: 2510, Score: 0.00018
Feature: 2511, Score: 0.00008
Feature: 2512, Score: 0.00004
Feature: 2513, Score: 0.00001
Feature: 2514, Score: 0.00006
Feature: 2515, Score: 0.00002
Feature: 2516, Score: 0.00007
Feature: 2517, Score: 0.00007
Feature: 2518, Score: 0.00008
Feature: 2519, Score: 0.00009
Feature: 2520, Score: 0.00003
Feature: 2521, Score: 0.00008
Feature: 2522, Score: 0.00003
Feature: 2523, Score: 0.00022
Feature: 2524, Score: 0.00019
Feature: 2525, Score: 0.00006
Feature: 2526, Score: 0.00010
Feature: 2527, Score: 0.00012
Feature: 2528, Score: 0.00007
Feature: 2529, Score: 0.00013
Feature: 2530, Score: 0.00035
Feature: 2531, Score: 0.00010
Feature: 2532, Score: 0.00015
Feature: 2533, Score: 0.00006
Feature: 2534, Score: 0.00015
Feature: 2535, Score: 0.00002
Feature: 2536, Score: 0.00023
Feature: 2537, Score: 0.00004
Feature: 2538, Score: 0.00002
Feature: 2539, Score: 0.00006
Feature: 2540, Score: 0.00005
Feature: 2541, Score: 0.00002
Feature: 2542, Score: 0.00003
Feature: 2543, Score: 0.00006
Feature: 2544, Score: 0.00021
Feature: 2545, Score: 0.00005
Feature: 2546, Score: 0.00006
Feature: 2547, Score: 0.00008
Feature: 2548, Score: 0.00010
Feature: 2549, Score: 0.00011

plt.bar([3800+2*x for x in range(len(importance))], importance)
plt.show()

indices = np.argsort(importance)[::-1]
print("Feature ranking:")
for f in range(X_train.shape[1]):
    print("%d. feature %d (%f)" % (f + 1, 3800+2*indices[f], importance[indices[f]]))

Feature ranking:
1. feature 8242 (0.040318)
2. feature 8090 (0.015053)
3. feature 8220 (0.014865)
4. feature 7904 (0.014450)
5. feature 3976 (0.013556)
6. feature 8244 (0.010650)
7. feature 3978 (0.010539)
8. feature 8238 (0.010312)
9. feature 8330 (0.010310)
10. feature 8010 (0.010310)
11. feature 8252 (0.010234)
12. feature 8098 (0.010202)
13. feature 8204 (0.009970)
14. feature 8138 (0.009953)
15. feature 8328 (0.009906)
16. feature 8156 (0.009893)
17. feature 8374 (0.009865)
18. feature 8178 (0.009807)
19. feature 8246 (0.009754)
20. feature 8050 (0.009497)
21. feature 4034 (0.007333)
22. feature 4038 (0.006462)
23. feature 8146 (0.005833)
24. feature 7436 (0.005817)
25. feature 8100 (0.005727)
26. feature 8094 (0.005702)
27. feature 4110 (0.005685)
28. feature 8372 (0.005617)
29. feature 8092 (0.005526)
30. feature 7848 (0.005500)
31. feature 7790 (0.005411)
32. feature 8096 (0.005402)
33. feature 8008 (0.005379)
34. feature 8072 (0.005358)
35. feature 4092 (0.005337)
36. feature 4012 (0.005218)
37. feature 8154 (0.005208)
38. feature 8222 (0.005184)
39. feature 8234 (0.005173)
40. feature 8046 (0.005149)
41. feature 3988 (0.005146)
42. feature 8376 (0.005144)
43. feature 8012 (0.005079)
44. feature 8048 (0.005072)
45. feature 8258 (0.005062)
46. feature 8210 (0.005031)
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2549. feature 5884 (0.000000)
2550. feature 7180 (0.000000)

cv_scores = cross_val_score(rf,X_train,y_train,cv=3)
mean = np.mean(cv_scores)

mean

0.9131272298232318

param_test= {'n_estimators':range(10,101,10)}  
gsearch= GridSearchCV(estimator = RandomForestClassifier(),  
                       param_grid =param_test, scoring='roc_auc',cv=5)
gsearch.fit(X_train,y_train)  

GridSearchCV(cv=5, error_score=nan,
             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                              class_weight=None,
                                              criterion='gini', max_depth=None,
                                              max_features='auto',
                                              max_leaf_nodes=None,
                                              max_samples=None,
                                              min_impurity_decrease=0.0,
                                              min_impurity_split=None,
                                              min_samples_leaf=1,
                                              min_samples_split=2,
                                              min_weight_fraction_leaf=0.0,
                                              n_estimators=100, n_jobs=None,
                                              oob_score=False,
                                              random_state=None, verbose=0,
                                              warm_start=False),
             iid='deprecated', n_jobs=None,
             param_grid={'n_estimators': range(10, 101, 10)},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring='roc_auc', verbose=0)

gsearch.best_params_

{'n_estimators': 90}

gsearch.best_score_

0.9723230639103975

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train_pca,y_train)

KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                     metric_params=None, n_jobs=None, n_neighbors=3, p=2,
                     weights='uniform')

y_predict_proba_knn = knn.predict_proba(X_test_pca)

y_predict_proba_knn

array([[0.        , 1.        ],
       [1.        , 0.        ],
       [1.        , 0.        ],
       ...,
       [0.        , 1.        ],
       [0.33333333, 0.66666667],
       [0.        , 1.        ]])

y_predict_knn = knn.predict(X_test_pca)

y_predict_knn

array([3, 2, 2, 3, 2, 2, 3, 2, 3, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 3,
       2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3,
       3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 3,
       3, 3, 3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2, 3, 3, 2, 3, 3, 2,
       2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 3, 2, 2, 2, 3, 3, 2, 3, 2, 3, 3, 3,
       3, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 2, 3, 2, 3, 2,
       2, 2, 3, 2, 2, 3, 3, 2, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 3, 2, 3, 2,
       3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2,
       3, 2, 2, 2, 3, 3, 3, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 2, 3, 3, 3,
       3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 2, 3, 3, 3, 2, 3, 2, 3, 2, 3, 2, 3,
       2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 3, 2, 3, 2, 2, 2,
       2, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 3, 3, 2, 2, 2, 3, 3,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2,
       2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 3,
       3, 3, 3, 2, 2, 2, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2,
       3, 3, 2, 2, 3, 2, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 3, 2, 3, 3,
       2, 3, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 3, 3, 2, 3, 2,
       2, 3, 3, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 3, 2, 2,
       2, 2, 3, 3, 3, 3, 3, 2, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 3,
       2, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 2, 3, 2, 3, 2,
       3, 2, 3, 2, 3, 2, 2, 3, 3, 3, 2, 3, 2, 2, 2, 3, 3, 3, 3, 2, 2, 3,
       3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, 2, 3, 3, 3, 2,
       3, 3, 3, 2, 2, 2, 3, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2,
       3, 2, 3, 3, 3, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2, 3, 2, 3, 3,
       2, 2, 3, 2, 2, 3, 3, 2, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,
       2, 2, 2, 3, 3, 2, 2, 3, 3, 2, 3, 2, 3, 2, 3, 3, 3, 2, 2, 2, 2, 2,
       2, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 3, 2,
       3, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2,
       3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 2, 2,
       3, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 3,
       2, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 3, 3, 2, 3, 2, 2, 3, 2, 3, 3, 3,
       2, 2, 2, 3, 3, 3, 3, 3, 2, 2, 2, 3, 2, 3, 3, 3, 2, 2, 3, 2, 3, 2,
       2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 3, 2, 2, 2, 3, 2, 2, 2, 3, 2, 3,
       2, 2, 2, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 2, 3, 3,
       2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 3,
       3, 3, 3, 3, 3, 3, 3, 3])

print(metrics.classification_report(y_test,y_predict_knn,target_names=['galaxy','qso']))

              precision    recall  f1-score   support

      galaxy       0.87      0.88      0.88       417
         qso       0.87      0.85      0.86       383

    accuracy                           0.87       800
   macro avg       0.87      0.87      0.87       800
weighted avg       0.87      0.87      0.87       800

metrics.roc_auc_score(y_test, y_predict_knn)

0.8681399527897264

metrics.plot_roc_curve(rf, X_test, y_test)
ax = plt.gca()
metrics.plot_roc_curve(knn, X_test, y_test,ax=ax)
plt.show()