Q&A
Python
1X = features_df.ix[:, 1:] 2y = features_df['labels'] 3 4# 学習データとテストデータの分離 5X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) 6 7# ランダムフォレストの作成 8forest = RandomForestClassifier(min_samples_leaf=3, random_state=0) 9forest.fit(X_train, y_train)
以上のコードを実行したところ、
Traceback (most recent call last): File "zyan07.py", line 85, in <module> forest.fit(X_train, y_train) File "/Users/anaconda/lib/python3.6/site-packages/sklearn/ensemble/forest.py", line 247, in fit X = check_array(X, accept_sparse="csc", dtype=DTYPE) File "/Users/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py", line 382, in check_array array = np.array(array, dtype=dtype, order=order, copy=copy) ValueError: setting an array element with a sequence.
以上のような要素の数が合わない時にでるエラーがでました。
しかし、
# fetures_df labels 1 \ 0 0 [2.97824915877e-05, 2.80212722019e-05, 3.12427... 1 1 [2.90123477039e-05, 3.26028408553e-05, 1.48843... 2 1 [2.56926697949e-05, 1.79988710521e-05, 1.01435... 3 2 [2.71302874928e-05, 3.19039861701e-05, 2.89806... 4 0 [4.83277936431e-05, 4.33511629455e-05, 6.40456... 5 2 [3.493801307e-05, 4.16113943935e-05, 3.8435169... 6 2 [2.3777129164e-05, 1.35680704131e-05, 2.270679... 7 1 [5.46208761299e-06, 1.08878124966e-05, 1.26878... 8 0 [1.63636161029e-05, 2.62342199006e-05, 2.63173... 9 2 [4.03476453555e-05, 3.95290569071e-05, 3.19161... 10 1 [3.00297221501e-05, 2.9226245877e-05, 4.221138... 11 2 [3.25128782391e-05, 3.35083535783e-05, 3.48355... 12 1 [2.47604797997e-05, 2.05030698413e-05, 1.51778... 13 0 [8.68808692136e-06, 1.68280551061e-05, 1.93896... 14 2 [6.42420452131e-05, 4.74558873891e-05, 4.30709... 15 0 [3.6057376593e-05, 3.82655766119e-05, 3.753037... 16 1 [2.12201183706e-05, 2.82715382307e-05, 4.04773... 17 0 [3.67945591969e-05, 1.42523043633e-05, 1.86241... 2 \ 0 [0.000179793438931, 0.00016980111083, 0.000150... 1 [0.000378058468364, 0.000402920816196, 0.00038... 2 [0.000437080637334, 0.000439238474843, 0.00050... 3 [0.000337024736642, 0.000339248916987, 0.00034... 4 [0.000149395217425, 0.000168878051393, 0.00013... 5 [0.000250754247033, 0.000143778683215, 0.00021... 6 [0.000173371384097, 0.000201550627058, 0.00021... 7 [0.000393213332408, 0.000313914550262, 0.00025... 8 [0.00019810411951, 1.55407381603e-05, 3.631823... 9 [0.000170623467018, 0.000244159899553, 0.00019... 10 [0.000148300649828, 9.85161498007e-05, 0.00032... 11 [0.000342794332115, 0.00038247713309, 0.000203... 12 [0.000348036418959, 0.0002342878669, 0.0002631... 13 [0.000186800504285, 0.000203872884652, 0.00020... 14 [0.000243373392654, 0.000301024432364, 0.00031... 15 [0.000612165794754, 0.000578596428796, 0.00058... 16 [0.000328922318758, 0.000283049355871, 0.00022... 17 [6.20945491609e-05, 0.000136985495518, 3.19985... 3 \ 0 [0.000386125394253, 0.00034886660647, 0.000329... 1 [0.000322340549072, 0.000321636315858, 0.00032... 2 [0.000346065668681, 0.000340986632929, 0.00034... 3 [0.000323317519447, 0.0003190396021, 0.0003064... 4 [0.000337555468417, 0.000324296562224, 0.00027... 5 [0.000362464802506, 0.000326058183764, 0.00033... 6 [0.000302001755417, 0.000299786572247, 0.00032... 7 [0.000282283340458, 0.000295502420171, 0.00027... 8 [0.000291887407688, 0.000274756733694, 0.00026... 9 [0.000265741974136, 0.000255527745826, 0.00026... 10 [0.000277773894822, 0.000260965388689, 0.00025... 11 [0.000256449414888, 0.000274864550022, 0.00024... 12 [0.0002701115417, 0.000250361926249, 0.0002658... 13 [0.000280118823715, 0.000258019893502, 0.00024... 14 [0.000213667147572, 0.000214590620345, 0.00020... 15 [0.000223881394701, 0.000230492820829, 0.00021... 16 [0.000247653324313, 0.000228984236698, 0.00021... 17 [0.000209043229247, 0.000218594998555, 0.00024... 4 \ 0 [0.00012928983491, 0.000117485219051, 0.000119... 1 [0.000149255279407, 0.000135313449054, 0.00013... 2 [0.000188674123859, 0.000192549212858, 0.00019... 3 [0.000198923468165, 0.000190331736346, 0.00018... 4 [0.000217834647528, 0.000220913107777, 0.00019... 5 [0.00024965778316, 0.000226133734398, 0.000234... 6 [0.000214257100472, 0.000224289377517, 0.00022... 7 [0.00021740502396, 0.00020217104498, 0.0001954... 8 [0.00021269651358, 0.000203517238561, 0.000193... 9 [0.000162400991773, 0.000166070778069, 0.00017... 10 [0.000168098758267, 0.000156010942486, 0.00014... 11 [0.000145344837335, 0.00014830539362, 0.000127... 12 [0.000128141799154, 0.000121540235526, 0.00013... 13 [0.000120852999511, 0.000118281673821, 0.00011... 14 [0.000100844937678, 9.56503527016e-05, 9.24594... 15 [8.82674764972e-05, 0.000103235988285, 9.51999... 16 [0.000113419695262, 9.13402981403e-05, 8.51897... 17 [9.98972071033e-05, 8.47902039909e-05, 9.65109... 5 \ 0 [5.06697731276e-05, 4.56213167671e-05, 4.17302... 1 [3.74393276154e-05, 4.6889924119e-05, 3.910347... 2 [1.11492800071e-05, 1.54983973879e-05, 1.95784... 3 [3.23907194534e-05, 2.07456991346e-05, 2.39975... 4 [4.79865173231e-05, 5.5817506601e-05, 4.662185... 5 [5.58489497215e-05, 4.33094432606e-05, 5.09702... 6 [5.02180310633e-05, 5.40110315375e-05, 5.01358... 7 [5.16985488852e-05, 4.65511410051e-05, 5.00481... 8 [4.45927247174e-05, 4.80092481716e-05, 5.22402... 9 [3.9563432476e-05, 4.09075797627e-05, 4.479366... 10 [4.33358827873e-05, 3.69181327848e-05, 2.07988... 11 [1.25660302807e-05, 1.71341019322e-05, 1.69243... 12 [9.58552507696e-06, 4.33846201343e-06, 1.89990... 13 [1.09003339642e-05, 7.40715781684e-06, 1.80630... 14 [1.58042349817e-05, 2.14539657158e-05, 3.00766... 15 [2.46813110151e-05, 1.83479730242e-05, 2.55376... 16 [2.05940684505e-05, 2.13031206209e-05, 7.43622... 17 [7.23697849416e-06, 5.73967771035e-06, 2.06132... 6 \ 0 [0.000909951371051, 0.00078391301343, 0.000865... 1 [0.00087228990885, 0.000899921568186, 0.000764... 2 [0.000789438440912, 0.000724688839521, 0.00081... 3 [0.000753696421766, 0.000722877975565, 0.00066... 4 [0.000663413720247, 0.000679354943919, 0.00048... 5 [0.000668080518036, 0.000630029615985, 0.00064... 6 [0.000673826650867, 0.00066376936332, 0.000731... 7 [0.000717818878518, 0.000728896336046, 0.00068... 8 [0.000700599034471, 0.000584293724519, 0.00058... 9 [0.000524804960353, 0.00057331709243, 0.000592... 10 [0.000544462613832, 0.000504994774169, 0.00048... 11 [0.000582041633712, 0.000598269097902, 0.00045... 12 [0.000501502367586, 0.000489994329355, 0.00060... 13 [0.000480992366111, 0.000516530411402, 0.00054... 14 [0.000494741306695, 0.000380927390787, 0.00040... 15 [0.000337586176551, 0.000455506712672, 0.00036... 16 [0.000518626031957, 0.000416718319296, 0.00036... 17 [0.000484213743961, 0.000461429094728, 0.00050... 7 \ 0 [3.6238285166e-05, 3.17065217717e-05, 4.019575... 1 [2.17795827295e-05, 2.44873997007e-05, 1.61078... 2 [1.85917744618e-05, 2.23762749496e-05, 1.00077... 3 [2.88442667942e-05, 2.54547357394e-05, 2.62418... 4 [3.92121690512e-05, 3.4004140183e-05, 2.476418... 5 [3.40228455232e-05, 3.20629321412e-05, 3.15090... 6 [2.37156477875e-05, 2.94728434253e-05, 3.93101... 7 [3.32231359943e-05, 3.26010383022e-05, 2.80378... 8 [4.01627911097e-05, 3.08963176924e-05, 3.09645... 9 [2.51317506814e-05, 3.52766006937e-05, 3.86308... 10 [3.60905153225e-05, 2.89001486955e-05, 2.78595... 11 [3.76929328923e-05, 4.02701263964e-05, 2.23868... 12 [3.23386986987e-05, 3.12657703805e-05, 3.54199... 13 [4.56603420081e-05, 4.57778175466e-05, 5.68947... 14 [3.820040599e-05, 2.23325608667e-05, 4.0332071... 15 [2.34477193999e-05, 4.10953684999e-05, 2.07690... 16 [3.61796125834e-05, 2.74565877756e-05, 3.00205... 17 [1.95650644988e-05, 1.3418310591e-05, 3.271473... 8 0 [3.26888591774e-05, 6.22149920978e-05, 2.08512... 1 [2.13746239841e-05, 2.91717897e-05, 1.67848268... 2 [1.58193353842e-05, 2.71193400762e-05, 1.61646... 3 [2.0520418166e-05, 1.19986974799e-05, 1.036316... 4 [2.66489577213e-05, 4.87720734369e-05, 1.57340... 5 [3.29022848666e-05, 1.5961953446e-05, 3.238837... 6 [3.76601374543e-05, 4.2154100685e-05, 3.329790... 7 [4.48956882478e-05, 1.92541886099e-05, 2.72667... 8 [3.91631249753e-05, 3.08750700914e-05, 3.78819... 9 [1.37154664548e-05, 3.15028994829e-05, 5.10265... 10 [3.33259361327e-05, 2.17747571578e-05, 1.94077... 11 [2.63040179548e-05, 2.97797667698e-05, 1.08294... 12 [1.16931762214e-05, 9.09241285619e-06, 1.53440... 13 [5.22224859999e-05, 4.74381585432e-05, 4.77211... 14 [2.66505307685e-05, 2.84150621355e-05, 4.48780... 15 [1.70663508688e-05, 4.55365831797e-05, 3.82321... 16 [5.79364263998e-05, 2.1250424711e-05, 2.724971... 17 [4.10983437211e-05, 1.33443850831e-05, 4.28582...
このようにfeature_dfの要素数は揃っています。
原因はどこに考えられるでしょうか。
よろしくお願い致します。
回答1件
0
ベストアンサー
'X_train.shape'はどうなっていますか？
len(fetures_df.iloc[0, '1']) == len(fetures_df.iloc[0, '2']) == ...になっている必要があります。
python
1a = np.array([np.arange(2), np.arange(3)]) 2print(a) 3#[array([0, 1]) array([0, 1, 2])] 4print(a.shape) 5#(2,) 6 7a = np.array([np.arange(3), np.arange(3)]) 8print(a) 9''' 10[[0 1 2] 11 [0 1 2]] 12''' 13print(a.shape) 14#(2, 3)
投稿2018/01/09 02:13
編集2018/01/09 03:56
総合スコア8560
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