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Commit 0e872aaf authored by YADNYESH SANTOSH PATIL's avatar YADNYESH SANTOSH PATIL
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code/.ipynb_checkpoints/preprocess-checkpoint.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.io as sp\n",
"from scipy.fftpack import fft\n",
"from scipy.io import wavfile # get the api\n",
"import sunau;\n",
"import librosa\n",
"import librosa.display\n",
"import pandas\n",
"import os\n",
"np.set_printoptions(suppress=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def writeMatrixToFile(self,filename,matrix):\n",
" np.save(filename, matrix);\n",
" os.rename(filename+\".npy\", filename+\".txt\");\n",
" print(\"Written successfully..\");\n",
" pass;\n",
" \n",
"def loadMatrixFromFile(self,filename): \n",
" matrix=None;\n",
" if(os.path.isfile(filename)):\n",
" matrix=np.load(filename); \n",
" return matrix;\n",
"\n",
"def readCSVFile(file):\n",
" data=pandas.read_csv(file,\",\",header=0, na_values='?', skipinitialspace=True);\n",
" return data;\n",
" pass;"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class PreProcessing:\n",
" filename=None;\n",
" y=None;\n",
" sr=None;\n",
" log_enabled=True;\n",
" centroid=None;\n",
" spectro=None;\n",
" spectro_phase=None;\n",
" max_sample_vector_size=660000; \n",
" duration=30;\n",
" def __init__(self,filename,duration=30):\n",
" #self.log(filename);\n",
" self.filename=filename;\n",
" self.reloadAudioFile(duration);\n",
" self.duration=duration;\n",
" pass;\n",
" \n",
" def reloadAudioFile(self,duration=30):\n",
" self.y, self.sr = librosa.load(self.filename,duration=duration);\n",
" self.y=self.y[:self.max_sample_vector_size];\n",
" pass;\n",
" \n",
" #Short-Term-Fourier trasform\n",
" def getSTFT(self):\n",
" self.stft=librosa.stft(y=self.y);\n",
" return self.stft;\n",
" pass;\n",
" \n",
" #spectro graph\n",
" def getSpectrogram(self):\n",
" stft=self.getSTFT();\n",
" self.spectro, self.spectro_phase = librosa.magphase(stft); \n",
" return self.spectro, self.spectro_phase;\n",
" pass;\n",
" \n",
" def getCentroid(self):\n",
" self.centroid=librosa.feature.spectral_centroid(y=self.y,sr=self.sr);\n",
" return self.centroid; \n",
"\n",
" def getSpectralRolloff(self):\n",
" self.rolloff=librosa.feature.spectral_rolloff(y=self.y, sr=self.sr);\n",
" return self.rolloff;\n",
" \n",
" def getZeroCrossing(self):\n",
" self.zero_crossing_rate=librosa.feature.zero_crossing_rate(self.y);\n",
" return self.zero_crossing_rate;\n",
" \n",
" def getSpectralContrast(self):\n",
" #Jiang, Dan-Ning, Lie Lu, Hong-Jiang Zhang, Jian-Hua Tao, and Lian-Hong Cai. “Music type classification by spectral contrast feature.” In Multimedia and Expo, 2002. ICME‘02. Proceedings. 2002 IEEE International Conference on, vol. 1, pp. 113-116. IEEE, 2002.\n",
" S = np.abs(self.getSTFT());\n",
" self.contrast = librosa.feature.spectral_contrast(S=S, sr=self.sr);\n",
" return self.contrast;\n",
" \n",
" def getMFCC(self):\n",
" self.mfcc = librosa.feature.mfcc(y=self.y, sr=self.sr, hop_length=512, n_mfcc=13);\n",
" return self.mfcc;\n",
" \n",
" def getChroma(self):\n",
" self.chroma = librosa.feature.chroma_stft(y=self.y,sr=self.sr,hop_length=512)\n",
" return self.chroma\n",
" \n",
" def getMelSpec(self):\n",
" self.mel=librosa.feature.melspectrogram(y=self.y, sr=self.sr,n_mels=10);\n",
" return self.mel;\n",
" \n",
" def getRMS(self):\n",
" self.rms=librosa.feature.rms(y=self.y);\n",
" return self.rms;\n",
"\n",
" def drawRMS(self):\n",
" rms=self.getRMS();\n",
" S,phase=self.getSpectrogram();\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" plt.semilogy(rms.T, label='RMS Energy')\n",
" plt.xticks([])\n",
" plt.xlim([0, rms.shape[-1]])\n",
" plt.legend(loc='best')\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),y_axis='log', x_axis='time')\n",
" plt.title('log Power spectrogram')\n",
" plt.tight_layout()\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawSpectrogramWithCentroid(self):\n",
" centroid=self.getCentroid();\n",
" S,phase=self.getSpectrogram();\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" plt.semilogy(centroid.T, label='Spectral centroid')\n",
" plt.ylabel('Hz')\n",
" plt.xticks([])\n",
" plt.xlim([0, centroid.shape[-1]])\n",
" plt.legend()\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),y_axis='log', x_axis='time')\n",
" plt.title('log Power spectrogram')\n",
" plt.tight_layout();\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawSpectralRolloff(self):\n",
" rolloff=self.getSpectralRolloff();\n",
" S,phase=self.getSpectrogram();\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" plt.semilogy(rolloff.T, label='Roll-off frequency')\n",
" plt.ylabel('Hz')\n",
" plt.xticks([])\n",
" plt.xlim([0, rolloff.shape[-1]])\n",
" plt.legend()\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),y_axis='log', x_axis='time')\n",
" plt.title('log Power spectrogram')\n",
" plt.tight_layout();\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawSpectralContrast(self):\n",
" contrast=self.getSpectralContrast();\n",
" S,phase=self.getSpectrogram();\n",
" S = np.abs(self.getSTFT());\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S,ref=np.max),y_axis='log')\n",
" plt.colorbar(format='%+2.0f dB')\n",
" plt.title('Power spectrogram')\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(contrast, x_axis='time')\n",
" plt.colorbar()\n",
" plt.ylabel('Frequency bands')\n",
" plt.title('Spectral contrast')\n",
" plt.tight_layout();\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawMFCC(self):\n",
" mfccs=self.getMFCC();\n",
" plt.figure(figsize=(6, 4))\n",
" librosa.display.specshow(mfccs, x_axis='time')\n",
" #plt.colorbar()\n",
" plt.title('MFCC')\n",
" plt.tight_layout()\n",
" plt.show();\n",
" pass;\n",
" \n",
" \n",
" def log(self,a,b=None):\n",
" if(self.log_enabled):\n",
" if(b!=None):\n",
" print(a,b);\n",
" else:\n",
" print(a);\n",
" pass; \n",
" \n",
" def estimate_tempo(self,oenv,sample_rate):\n",
" return librosa.beat.tempo(oenv,sr=sample_rate)\n",
"\n",
" def tempogram_analysis(self,signal, sample_rate, max_tempo = 320.0,hop_length = 512):\n",
" tempogram = librosa.feature.tempogram(y=signal, sr = sample_rate)\n",
" bpms = librosa.core.tempo_frequencies(tempogram.shape[0], hop_length=hop_length, sr=sample_rate)\n",
" means_over_time = np.mean(tempogram, axis = 1)\n",
" #account for max_tempo\n",
" if max_tempo is not None:\n",
" max_idx = np.argmax(bpms < max_tempo)\n",
" means_over_time[:max_idx] = 0 \n",
" top_five_tempos = np.argpartition(means_over_time, -3)[-3:-1]\n",
" top_five_tempo_values = means_over_time[top_five_tempos]\n",
" return bpms[top_five_tempos], top_five_tempo_values\n",
" \n",
" def getRhythm_features(self):\n",
" oenv = librosa.onset.onset_strength(y=self.y, sr=self.sr)\n",
" tempo = self.estimate_tempo(oenv,self.sr)\n",
" top_five_tempos, top_five_tempo_values = self.tempogram_analysis(self.y, self.sr)\n",
" tempos = np.append(top_five_tempos, top_five_tempo_values)\n",
" return np.append(tempos, tempo)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class ProcessDataset:\n",
" columns = [\"id\",\"type\",\"y_index\",\"y\",\"centroid mean\",\"rolloff mean\",\"zero mean\",\"rms mean\",\"rms var\",\"contrast mean\",\"contrast var\", \"mfcc1 mean\",\"mfcc1 var\",\n",
" \"mfcc2 mean\",\"mfcc2 var\",\"mfcc3 mean\",\"mfcc3 var\",\"mfcc4 mean\",\"mfcc4 var\",\"mfcc5 mean\",\"mfcc5 var\",\n",
" \"mel1 mean\",\"mel1 var\",\"mel2 mean\",\"mel2 var\",\"mel3 mean\",\"mel3 var\",\"mel4 mean\",\"mel4 var\",\"mel5 mean\", \"mel5 var\"]+[\"chroma_mean_\"+str(i) for i in range(1,7)]+ [\"chroma_var_\"+str(i) for i in range(1,7)]+[\"bpm_second\", \"bm_first\", \"tempo_strength_second\"] \n",
" #columns = [\"id\",\"type\",\"y_index\",\"y\",\"centroid mean\",\"centroid var\",\"rolloff mean\",\"rolloff var\",\"zero mean\",\n",
" # \"zero var\",\"rms mean\",\"rms var\",\"contrast mean\",\"contrast var\",\"mfcc1 mean\",\"mfcc1 var\",\n",
" # \"mfcc2 mean\",\"mfcc2 var\",\"mfcc3 mean\",\"mfcc3 var\",\"mfcc4 mean\",\"mfcc4 var\",\"mfcc5 mean\",\"mfcc5 var\",\n",
" # \"mfcc6 mean\",\"mfcc6 var\",\"mfcc7 mean\",\"mfcc7 var\",\"mfcc8 mean\",\"mfcc8 var\",\"mfcc9 mean\",\"mfcc9 var\",\n",
" # \"mfcc10 mean\",\"mfcc10 var\",\"mfcc11 mean\",\"mfcc11 var\",\"mfcc12 mean\",\"mfcc12 var\",\"mfcc13 mean\",\"mfcc13 var\"];\n",
"# +[\"chroma_mean_\"+str(i) for i in range(1,13)]+ [\"chroma_var_\"+str(i) for i in range(1,13)]+[\"bpm_second\", \"bm_first\", \"tempo_strength_second\"]\n",
" genre_out={\"blues\":[1,0,0,0,0,0,0,0,0,0],\"classical\":[0,1,0,0,0,0,0,0,0,0],\"country\":[0,0,1,0,0,0,0,0,0,0],\"disco\":[0,0,0,1,0,0,0,0,0,0],\"hiphop\":[0,0,0,0,1,0,0,0,0,0],\"jazz\":[0,0,0,0,0,1,0,0,0,0],\"metal\":[0,0,0,0,0,0,1,0,0,0],\"pop\":[0,0,0,0,0,0,0,1,0,0],\"reggae\":[0,0,0,0,0,0,0,0,1,0],\"rock\":[0,0,0,0,0,0,0,0,0,1]}; \n",
" genre_out_index={\"blues\":0,\"classical\":1,\"country\":2,\"disco\":3,\"hiphop\":4,\"jazz\":5,\"metal\":6,\"pop\":7,\"reggae\":8,\"rock\":9}; \n",
" dataframe=None;\n",
" mfcc_features=5;\n",
" mel_features=5;\n",
" chroma_features=6;\n",
" dir=\"../genres\";\n",
" genre_dir={\"blues\":\"blues\",\"classical\":\"classical\",\"country\":\"country\",\"disco\":\"disco\",\"hiphop\":\"hiphop\",\"jazz\":\"jazz\",\"metal\":\"metal\",\"pop\":\"pop\",\"reggae\":\"reggae\",\"rock\":\"rock\"};\n",
" def __init__(self):\n",
" self.dataframe = pandas.DataFrame(columns=self.columns);\n",
" print(\"Colums\",len(self.columns))\n",
" pass;\n",
"\n",
" def extractTimberalFeatures(self,genre,audio_number,filename):\n",
" features=[];\n",
" pp=PreProcessing(filename); \n",
" centroid=pp.getCentroid()[0];\n",
" rolloff=pp.getSpectralRolloff()[0]; \n",
" zero=pp.getZeroCrossing()[0]; \n",
" contrast=pp.getSpectralContrast()[0]; \n",
" rms=pp.getRMS()[0];\n",
" mfcc=pp.getMFCC();\n",
" mel=pp.getMelSpec();\n",
" chroma = pp.getChroma();\n",
" rhythm = pp.getRhythm_features();\n",
" \n",
" features.append(audio_number);\n",
" features.append(genre); \n",
" features.append(self.genre_out_index[genre]); \n",
" features.append(self.genre_out[genre]);\n",
" features.append(centroid.mean());\n",
"# features.append(centroid.var()); \n",
" features.append(rolloff.mean());\n",
"# features.append(rolloff.var()); \n",
" features.append(zero.mean());\n",
"# features.append(zero.var()); \n",
" features.append(rms.mean());\n",
" features.append(rms.var()); \n",
" features.append(contrast.mean());\n",
" features.append(contrast.var()); \n",
" for i in range(self.mfcc_features):\n",
" features.append(mfcc[i].mean());\n",
" features.append(mfcc[i].var()); \n",
" for i in range(self.mel_features):\n",
" features.append(mel[i].mean());\n",
" features.append(mel[i].var());\n",
" for i in range(self.chroma_features):\n",
" features.append(chroma[i].mean());\n",
" features.append(chroma[i].var());\n",
" #features.append(np.mean(chroma,axis=1).reshape(1,12));\n",
" #features.append(np.var(chroma,axis=1).reshape(1,12));\n",
" #print(np.mean(chroma,axis=1).shape);\n",
" #print(\"Df\",self.dataframe.size)\n",
" #print(\"Features\",np.array(features).shape)\n",
" #print(rhythm.shape)\n",
" for i in range(3):\n",
" features.append(rhythm[i]);\n",
" self.dataframe.loc[self.dataframe.size]=features;\n",
" \n",
" def saveDataFrame(self):\n",
" filename=\"audiofeatures_numpy_matrix\";\n",
" arr=self.dataframe.as_matrix(columns=None);\n",
" np.save(filename,arr); \n",
" self.dataframe.to_csv(\"audiofeatures.csv\",sep=\",\")\n",
" print(\"Written successfully..\");\n",
" pass;\n",
" \n",
" def extractFeatures(self):\n",
" print(\"Extracting Features...\");\n",
" percent_completed=0; \n",
" for k in self.genre_dir: \n",
" genre=k;\n",
" print(\"-------------------[\"+genre+\"]-----------------------\")\n",
" for i in range(100):\n",
" audio_number=\"%0.5d\"%i;\n",
" filename=self.dir+\"/\"+self.genre_dir[genre]+\"/\"+self.genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
" self.extractTimberalFeatures(genre,audio_number,filename); \n",
" percent_completed=i;\n",
" if(percent_completed%10==0):\n",
" print(\"Percent completed:\",percent_completed); \n",
" self.saveDataFrame();\n",
" print(\"Extraction done\");\n",
" pass;\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Colums 46\n",
"Extracting Features...\n",
"-------------------[blues]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n"
]
}
],
"source": [
"p1=ProcessDataset();\n",
"p1.extractFeatures();\n",
"p1.dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dir=\"../genres\";\n",
"genre_dir={\"blues\":\"blues\",\"classical\":\"classical\",\"country\":\"country\",\"disco\":\"disco\",\"hiphop\":\"hiphop\",\"jazz\":\"jazz\",\"metal\":\"metal\",\"pop\":\"pop\",\"reggae\":\"reggae\",\"rock\":\"rock\"};"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"genre=\"disco\"\n",
"\n",
"audio_number=\"%0.5d\"%0;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"\n",
"pp=PreProcessing(filename); \n",
"s,p=pp.getSpectrogram();\n",
"len(s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"genre=\"disco\"\n",
"audio_number=\"%0.5d\"%0;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"\n",
"pp=PreProcessing(filename); \n",
"a=librosa.feature.melspectrogram(y=pp.y, sr=pp.sr,n_mels=10)\n",
"len(a[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"genre=\"disco\"\n",
"pds=ProcessDataset();\n",
"\n",
"audio_number=\"%0.5d\"%98;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"pds.extractTimberalFeatures(genre,audio_number,filename);\n",
"\n",
"audio_number=\"%0.5d\"%99;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"pds.extractTimberalFeatures(genre,audio_number,filename);\n",
"\n",
"audio_number=\"%0.5d\"%0;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"pds.extractTimberalFeatures(genre,audio_number,filename);\n",
"\n",
"#pds.extractFeatures();\n",
"pds.dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def partitionDataFrame(df,ratio):\n",
" df = df.sample(frac=1).reset_index(drop=True)#shuffling rows\n",
" df = df.sample(frac=1).reset_index(drop=True)#again shuffling\n",
" size=df[\"id\"].count();\n",
" limit=int(ratio*size);\n",
" train_ds=df.loc[0:limit]; \n",
" test_ds=df.loc[limit:size];\n",
" train_ds.to_csv(\"train.csv\",sep=\",\");\n",
" test_ds.to_csv(\"test.csv\",sep=\",\");\n",
" print(\"Partitioning done\");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df=readCSVFile(\"audiofeatures.csv\");\n",
"partitionDataFrame(df,0.9);\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
code/.ipynb_checkpoints/trainNtest-checkpoint.ipynb 0 → 100644
View file @ 0e872aaf
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas;\n",
"import numpy as np;\n",
"import ast;\n",
"import sklearn.tree as tree;\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.multiclass import OneVsRestClassifier\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn import datasets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def readCSVFile(file):\n",
" data=pandas.read_csv(file,\",\",header=0, na_values='?', skipinitialspace=True);\n",
" return data;\n",
" pass;\n",
"def readTrainData(dataset): \n",
" return dataset.iloc[:,6:], dataset.iloc[:,4:5].astype(int),dataset.iloc[:,5:6];\n",
" pass;\n",
"\n",
"def readTestData(dataset): \n",
" return dataset.iloc[:,6:], dataset.iloc[:,4:5].astype(int),dataset.iloc[:,5:6];\n",
" pass;\n",
"\n",
"def normalizePhi(unNormalizedPhi,last_col_bias=False): \n",
" #assuming last column as bias column\n",
" no_of_column=len(unNormalizedPhi[0]);\n",
" phi=np.array(unNormalizedPhi);\n",
" std=phi.std(0);\n",
" mean=phi.mean(0); \n",
" #std[no_of_column-1]=1;\n",
" #mean[no_of_column-1]=0;\n",
" #phi_normalize=(phi-mean)/std; \n",
" \n",
" max_vector=phi.max(axis=0)\n",
" phi_normalize=phi/max_vector; \n",
" \n",
" return phi_normalize;\n",
" pass;\n",
"\n",
"def categoryToNumber(dataset,categoryList):\n",
" for c in categoryList:\n",
" if (c in dataset): \n",
" dataset[c]=pandas.get_dummies(dataset[c]).values.argmax(1); \n",
" return dataset;\n",
" pass;\n",
" \n",
"\n",
"def handleCategoryData(dataset,categoryList): \n",
" return categoryToNumber(dataset,categoryList)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n",
" 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris = datasets.load_iris()\n",
"X, y = iris.data, iris.target\n",
"OneVsRestClassifier(LinearSVC(random_state=0,max_iter=4000)).fit(X, y).predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" if __name__ == '__main__':\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:17: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:18: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:19: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
]
}
],
"source": [
"dir=\"./\"\n",
"trainFile=dir+\"train.csv\";\n",
"testFile=dir+\"test.csv\";\n",
"trained_dataset=readCSVFile(trainFile);\n",
"test_dataset=readCSVFile(testFile);\n",
"trained_data,trained_y,trained_y_vector=readTrainData(trained_dataset);\n",
"test_data,test_y,test_y_vector=readTestData(test_dataset);\n",
"\n",
"mtx_train =trained_data.as_matrix(columns=None)\n",
"mtx_train_y =trained_y.as_matrix(columns=None)\n",
"mtx_trained_y_vector=trained_y_vector.as_matrix(columns=None);\n",
"\n",
"mtx_train_norm=normalizePhi(mtx_train);\n",
"mtx_train_y=np.array(list((e[0] for e in mtx_train_y)));\n",
"mtx_trained_y_vector=np.array(list((ast.literal_eval(e[0]) for e in mtx_trained_y_vector)));\n",
"\n",
"mtx_test=test_data.as_matrix(columns=None);\n",
"mtx_test_y=test_y.as_matrix(columns=None);\n",
"mtx_test_y_vector=test_y_vector.as_matrix(columns=None);\n",
"\n",
"mtx_test_norm=normalizePhi(mtx_test);\n",
"mtx_test_y=np.array(list((e[0] for e in mtx_test_y)));\n",
"mtx_test_y_vector=np.array(list((ast.literal_eval(e[0]) for e in mtx_test_y_vector)));"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5 9 7 9 8 6 6 9 1 9 9 8 7 7 9 1 9 9 7 6 9 6 2 9 9 5 1 1 9 9 6 9 9 0 7 9 9\n",
" 5 9 0 7 7 9 7 1 9 5 8 8 1 9 7 6 9 6 9 7 7 1 9 9 9 7 1 9 9 1 8 7 8 9 9 7 5\n",
" 9 9 7 9 7 7 1 0 6 7 9 9 9 1 5 0 7 9 9 9 1 7 9 1 7 9 5 0 9 6 7 6 9 1 5 6 1\n",
" 5 8 0 9 9 6 9 5 7 1 9 1 9 7 9 9 9 1 8 9 1 9 7 5 5 7 1 9 6 9 1 5 7 7 8 1 5\n",
" 9 8 7 9 9 1 7 9 6 7 0 0 1 5 7 7 9 2 5 7 7 9 7 0 6 1 9 9 1 1 9 1 9 7 1 5 4\n",
" 1 6 8 9 7 1 8 1 6 0 9 9 9 9 9 9 9 6 6 4 8 9 9 8 6 7 6 8 1 7 1 9 5 6 6 1 1\n",
" 9 9 7 7 7 9 5 6 7 7 9 0 0 1 9 8 5 2 7 9 9 7 1 7 7 0 9 5 9 7 9 7 6 8 5 7 1\n",
" 1 9 9 9 7 6 7 5 7 1 7 7 9 4 1 9 7 1 9 6 9 1 9 6 7 1 7 1 9 7 1 1 6 5 6 1 6\n",
" 9 1 9 9 1 0 5 7 9 9 7 4 1 9 6 7 7 9 5 9 6 9 9 7 8 5 8 0 9 9 9 7 5 9 1 9 5\n",
" 7 9 1 9 9 9 7 9 9 8 0 9 7 9 9 9 9 9 1 9 9 9 7 9 5 9 9 7 4 1 9 9 1 9 6 1 9\n",
" 1 9 6 1 7 9 7 1 8 9 1 1 7 6 9 7 9 7 6 7 9 9 7 9 9 5 9 7 7 9 8 6 6 7 7 5 9\n",
" 9 8 9 1 9 0 9 7 0 7 9 4 7 8 1 2 7 7 0 6 6 5 9 9 7 9 9 9 2 9 7 9 5 6 9 9 7\n",
" 9 9 6 8 5 6 9 7 9 9 9 1 6 7 6 5 9 9 9 7 7 7 0 8 9 9 9 9 5 6 9 5 9 6 7 9 7\n",
" 5 6 9 0 6 6 7 0 9 8 6 6 9 5 1 7 7 0 9 9 0 0 1 1 9 9 9 6 9 5 8 9 9 9 9 7 9\n",
" 9 9 9 0 7 5 9 5 6 9 0 1 7 7 6 5 7 9 1 1 8 7 7 1 0 5 9 7 5 9 7 7 8 5 9 5 6\n",
" 6 6 9 9 1 1 8 1 9 9 9 7 7 4 7 0 9 7 5 9 0 1 7 2 9 1 5 9 1 9 9 7 1 7 1 0 9\n",
" 8 9 1 9 9 7 9 9 9 9 7 1 7 1 9 1 6 9 9 5 7 7 9 7 9 6 5 6 7 6 6 6 8 1 1 7 9\n",
" 6 6 7 1 1 9 7 9 1 1 9 5 9 7 0 1 8 0 1 7 7 5 9 7 6 1 5 8 9 7 9 9 9 7 7 8 7\n",
" 9 7 7 0 7 8 9 9 1 9 9 7 6 9 0 5 9 7 7 9 5 1 9 7 9 6 5 1 9 1 6 7 7 9 8 5 8\n",
" 7 0 7 6 7 9 5 9 9 1 7 9 1 9 9 6 1 4 9 9 7 2 6 9 5 1 5 9 0 7 7 5 1 9 9 6 9\n",
" 7 9 6 9 9 9 9 9 1 1 1 0 7 9 9 7 0 8 7 8 6 7 1 1 1 1 1 7 1 1 5 7 1 9 7 9 9\n",
" 8 6 9 9 7 9 8 6 9 5 4 6 0 9 7 9 9 0 9 7 0 5 9 1 7 7 9 8 9 5 9 1 9 6 9 6 9\n",
" 0 7 1 9 9 7 0 9 9 7 5 6 1 5 5 7 9 1 9 8 0 9 5 5 6 5 9 5 5 9 9 5 6 5 1 9 7\n",
" 7 1 9 1 7 6 1 7 9 7 8 6 1 9 5 9 7 8 5 9 7 0 6 7 7 9 0 9 9 1 8 8 6 8 9 7 8\n",
" 1 1 9 5 1 7 0 6 7 1 9 5 9]\n",
"done\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" \"the number of iterations.\", ConvergenceWarning)\n"
]
}
],
"source": [
"# #trainedModel=OneVsRestClassifier(LinearSVC()).fit(mtx_train, mtx_train_y);\n",
"# trainedModel=LinearSVC(max_iter=12000).fit(mtx_train, mtx_train_y);\n",
"# y_predict=trainedModel.predict(mtx_train);\n",
"# print(y_predict);\n",
"# print(\"done\");"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[9 8 5 8 7 8 6 9 5 0 7 1 7 9 9 5 9 9 9 6 5 6 1 7 1 7 1 1 6 9 9 0 6 9 9 8 9\n",
" 9 9 9 9 5 8 7 6 0 9 1 7 9 0 5 9 9 9 9 7 1 1 5 0 9 7 9 5 6 9 6 9 9 0 9 9 5\n",
" 9 9 9 1 6 1 6 0 9 7 6 6 6 0 1 9 7 7 6 9 1 7 9 9 0 1]\n"
]
}
],
"source": [
"y_predict=trainedModel.predict(mtx_test);\n",
"print(y_predict);"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(901, 42)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" \"the number of iterations.\", ConvergenceWarning)\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
" \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Trained Misclassified Points</th>\n",
" <th>Trained Accuracy</th>\n",
" <th>Test Misclassified Points</th>\n",
" <th>Test Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Decision Tree</td>\n",
" <td>191</td>\n",
" <td>78.8013</td>\n",
" <td>58</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Random Forest</td>\n",
" <td>50</td>\n",
" <td>94.4506</td>\n",
" <td>38</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <td>K-Neighbors</td>\n",
" <td>323</td>\n",
" <td>64.1509</td>\n",
" <td>61</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Logistic Regression</td>\n",
" <td>279</td>\n",
" <td>69.0344</td>\n",
" <td>35</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Support Vector</td>\n",
" <td>433</td>\n",
" <td>51.9423</td>\n",
" <td>49</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Bernoulli NB</td>\n",
" <td>711</td>\n",
" <td>21.0877</td>\n",
" <td>81</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Gaussian NB</td>\n",
" <td>413</td>\n",
" <td>54.162</td>\n",
" <td>48</td>\n",
" <td>52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Trained Misclassified Points Trained Accuracy \\\n",
"Decision Tree 191 78.8013 \n",
"Random Forest 50 94.4506 \n",
"K-Neighbors 323 64.1509 \n",
"Logistic Regression 279 69.0344 \n",
"Support Vector 433 51.9423 \n",
"Bernoulli NB 711 21.0877 \n",
"Gaussian NB 413 54.162 \n",
"\n",
" Test Misclassified Points Test Accuracy \n",
"Decision Tree 58 42 \n",
"Random Forest 38 62 \n",
"K-Neighbors 61 39 \n",
"Logistic Regression 35 65 \n",
"Support Vector 49 51 \n",
"Bernoulli NB 81 19 \n",
"Gaussian NB 48 52 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = trained_data\n",
"print(x.shape)\n",
"y = np.ravel(trained_y)\n",
"y_test=np.ravel(test_y);\n",
"\n",
"index = [\"Decision Tree\", \"Random Forest\", \"K-Neighbors\",\n",
" \"Logistic Regression\", \"Support Vector\", \"Bernoulli NB\", \"Gaussian NB\"]\n",
"columns = [\"Trained Misclassified Points\", \"Trained Accuracy\", \"Test Misclassified Points\", \"Test Accuracy\",]\n",
"modelComparision = pandas.DataFrame(index=index, columns=columns)\n",
"\n",
"dtc = 0, DecisionTreeClassifier(min_samples_split=20)\n",
"rfc = 1, RandomForestClassifier(min_samples_split=10)\n",
"knn = 2, KNeighborsClassifier()\n",
"lgr = 3, LogisticRegression(max_iter=20000)\n",
"svc = 4, LinearSVC(max_iter=15000)\n",
"bnb = 5, BernoulliNB()\n",
"gnb = 6, GaussianNB()\n",
"\n",
"tr_size=len(x.index);\n",
"tt_size=len(y_test);\n",
"for i in lgr, gnb, bnb, dtc, svc, knn, rfc: \n",
" model=i[1].fit(x, y);\n",
" y_pred = model.predict(x)\n",
" tr_misclassifedPoints = (y_pred != y).sum() \n",
" tr_accuracy = ((tr_size - tr_misclassifedPoints)*100) / tr_size;\n",
" \n",
" y_pred = model.predict(test_data)\n",
" tt_misclassifedPoints = (y_pred != y_test).sum() \n",
" tt_accuracy = ((tt_size - tt_misclassifedPoints)*100) / tt_size;\n",
" if(i[0]==0):\n",
" tree.export_graphviz(model,out_file='tree.dot'); \n",
" #print(misclassifedPoints,\":\",size - misclassifedPoints,':',accuracy); \n",
" modelComparision.ix[i[0]] = [tr_misclassifedPoints, tr_accuracy, tt_misclassifedPoints, tt_accuracy,];\n",
" \n",
"print(\"Done\");\n",
"modelComparision"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
code/audiofeatures.csv 0 → 100644
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code/preprocess.ipynb 0 → 100644
View file @ 0e872aaf
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.io as sp\n",
"from scipy.fftpack import fft\n",
"from scipy.io import wavfile # get the api\n",
"import sunau;\n",
"import librosa\n",
"import librosa.display\n",
"import pandas\n",
"import os\n",
"np.set_printoptions(suppress=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def writeMatrixToFile(self,filename,matrix):\n",
" np.save(filename, matrix);\n",
" os.rename(filename+\".npy\", filename+\".txt\");\n",
" print(\"Written successfully..\");\n",
" pass;\n",
" \n",
"def loadMatrixFromFile(self,filename): \n",
" matrix=None;\n",
" if(os.path.isfile(filename)):\n",
" matrix=np.load(filename); \n",
" return matrix;\n",
"\n",
"def readCSVFile(file):\n",
" data=pandas.read_csv(file,\",\",header=0, na_values='?', skipinitialspace=True);\n",
" return data;\n",
" pass;"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class PreProcessing:\n",
" filename=None;\n",
" y=None;\n",
" sr=None;\n",
" log_enabled=True;\n",
" centroid=None;\n",
" spectro=None;\n",
" spectro_phase=None;\n",
" max_sample_vector_size=660000; \n",
" duration=30;\n",
" def __init__(self,filename,duration=30):\n",
" #self.log(filename);\n",
" self.filename=filename;\n",
" self.reloadAudioFile(duration);\n",
" self.duration=duration;\n",
" pass;\n",
" \n",
" def reloadAudioFile(self,duration=30):\n",
" self.y, self.sr = librosa.load(self.filename,duration=duration);\n",
" self.y=self.y[:self.max_sample_vector_size];\n",
" pass;\n",
" \n",
" #Short-Term-Fourier trasform\n",
" def getSTFT(self):\n",
" self.stft=librosa.stft(y=self.y);\n",
" return self.stft;\n",
" pass;\n",
" \n",
" #spectro graph\n",
" def getSpectrogram(self):\n",
" stft=self.getSTFT();\n",
" self.spectro, self.spectro_phase = librosa.magphase(stft); \n",
" return self.spectro, self.spectro_phase;\n",
" pass;\n",
" \n",
" def getCentroid(self):\n",
" self.centroid=librosa.feature.spectral_centroid(y=self.y,sr=self.sr);\n",
" return self.centroid; \n",
"\n",
" def getSpectralRolloff(self):\n",
" self.rolloff=librosa.feature.spectral_rolloff(y=self.y, sr=self.sr);\n",
" return self.rolloff;\n",
" \n",
" def getZeroCrossing(self):\n",
" self.zero_crossing_rate=librosa.feature.zero_crossing_rate(self.y);\n",
" return self.zero_crossing_rate;\n",
" \n",
" def getSpectralContrast(self):\n",
" #Jiang, Dan-Ning, Lie Lu, Hong-Jiang Zhang, Jian-Hua Tao, and Lian-Hong Cai. “Music type classification by spectral contrast feature.” In Multimedia and Expo, 2002. ICME‘02. Proceedings. 2002 IEEE International Conference on, vol. 1, pp. 113-116. IEEE, 2002.\n",
" S = np.abs(self.getSTFT());\n",
" self.contrast = librosa.feature.spectral_contrast(S=S, sr=self.sr);\n",
" return self.contrast;\n",
" \n",
" def getMFCC(self):\n",
" self.mfcc = librosa.feature.mfcc(y=self.y, sr=self.sr, hop_length=512, n_mfcc=13);\n",
" return self.mfcc;\n",
" \n",
" def getChroma(self):\n",
" self.chroma = librosa.feature.chroma_stft(y=self.y,sr=self.sr,hop_length=512)\n",
" return self.chroma\n",
" \n",
" def getMelSpec(self):\n",
" self.mel=librosa.feature.melspectrogram(y=self.y, sr=self.sr,n_mels=10);\n",
" return self.mel;\n",
" \n",
" def getRMS(self):\n",
" self.rms=librosa.feature.rms(y=self.y);\n",
" return self.rms;\n",
"\n",
" def drawRMS(self):\n",
" rms=self.getRMS();\n",
" S,phase=self.getSpectrogram();\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" plt.semilogy(rms.T, label='RMS Energy')\n",
" plt.xticks([])\n",
" plt.xlim([0, rms.shape[-1]])\n",
" plt.legend(loc='best')\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),y_axis='log', x_axis='time')\n",
" plt.title('log Power spectrogram')\n",
" plt.tight_layout()\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawSpectrogramWithCentroid(self):\n",
" centroid=self.getCentroid();\n",
" S,phase=self.getSpectrogram();\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" plt.semilogy(centroid.T, label='Spectral centroid')\n",
" plt.ylabel('Hz')\n",
" plt.xticks([])\n",
" plt.xlim([0, centroid.shape[-1]])\n",
" plt.legend()\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),y_axis='log', x_axis='time')\n",
" plt.title('log Power spectrogram')\n",
" plt.tight_layout();\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawSpectralRolloff(self):\n",
" rolloff=self.getSpectralRolloff();\n",
" S,phase=self.getSpectrogram();\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" plt.semilogy(rolloff.T, label='Roll-off frequency')\n",
" plt.ylabel('Hz')\n",
" plt.xticks([])\n",
" plt.xlim([0, rolloff.shape[-1]])\n",
" plt.legend()\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max),y_axis='log', x_axis='time')\n",
" plt.title('log Power spectrogram')\n",
" plt.tight_layout();\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawSpectralContrast(self):\n",
" contrast=self.getSpectralContrast();\n",
" S,phase=self.getSpectrogram();\n",
" S = np.abs(self.getSTFT());\n",
" plt.figure()\n",
" plt.subplot(2, 1, 1)\n",
" librosa.display.specshow(librosa.amplitude_to_db(S,ref=np.max),y_axis='log')\n",
" plt.colorbar(format='%+2.0f dB')\n",
" plt.title('Power spectrogram')\n",
" plt.subplot(2, 1, 2)\n",
" librosa.display.specshow(contrast, x_axis='time')\n",
" plt.colorbar()\n",
" plt.ylabel('Frequency bands')\n",
" plt.title('Spectral contrast')\n",
" plt.tight_layout();\n",
" plt.show();\n",
" pass;\n",
" \n",
" def drawMFCC(self):\n",
" mfccs=self.getMFCC();\n",
" plt.figure(figsize=(6, 4))\n",
" librosa.display.specshow(mfccs, x_axis='time')\n",
" #plt.colorbar()\n",
" plt.title('MFCC')\n",
" plt.tight_layout()\n",
" plt.show();\n",
" pass;\n",
" \n",
" \n",
" def log(self,a,b=None):\n",
" if(self.log_enabled):\n",
" if(b!=None):\n",
" print(a,b);\n",
" else:\n",
" print(a);\n",
" pass; \n",
" \n",
" def estimate_tempo(self,oenv,sample_rate):\n",
" return librosa.beat.tempo(oenv,sr=sample_rate)\n",
"\n",
" def tempogram_analysis(self,signal, sample_rate, max_tempo = 320.0,hop_length = 512):\n",
" tempogram = librosa.feature.tempogram(y=signal, sr = sample_rate)\n",
" bpms = librosa.core.tempo_frequencies(tempogram.shape[0], hop_length=hop_length, sr=sample_rate)\n",
" means_over_time = np.mean(tempogram, axis = 1)\n",
" #account for max_tempo\n",
" if max_tempo is not None:\n",
" max_idx = np.argmax(bpms < max_tempo)\n",
" means_over_time[:max_idx] = 0 \n",
" top_five_tempos = np.argpartition(means_over_time, -3)[-3:-1]\n",
" top_five_tempo_values = means_over_time[top_five_tempos]\n",
" return bpms[top_five_tempos], top_five_tempo_values\n",
" \n",
" def getRhythm_features(self):\n",
" oenv = librosa.onset.onset_strength(y=self.y, sr=self.sr)\n",
" tempo = self.estimate_tempo(oenv,self.sr)\n",
" top_five_tempos, top_five_tempo_values = self.tempogram_analysis(self.y, self.sr)\n",
" tempos = np.append(top_five_tempos, top_five_tempo_values)\n",
" return np.append(tempos, tempo)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class ProcessDataset:\n",
" columns = [\"id\",\"type\",\"y_index\",\"y\",\"centroid mean\",\"rolloff mean\",\"zero mean\",\"rms mean\",\"rms var\",\"contrast mean\",\"contrast var\", \"mfcc1 mean\",\"mfcc1 var\",\n",
" \"mfcc2 mean\",\"mfcc2 var\",\"mfcc3 mean\",\"mfcc3 var\",\"mfcc4 mean\",\"mfcc4 var\",\"mfcc5 mean\",\"mfcc5 var\",\n",
" \"mel1 mean\",\"mel1 var\",\"mel2 mean\",\"mel2 var\",\"mel3 mean\",\"mel3 var\",\"mel4 mean\",\"mel4 var\",\"mel5 mean\", \"mel5 var\"]+[\"chroma_mean_\"+str(i) for i in range(1,7)]+ [\"chroma_var_\"+str(i) for i in range(1,7)]+[\"bpm_second\", \"bm_first\", \"tempo_strength_second\"] \n",
" #columns = [\"id\",\"type\",\"y_index\",\"y\",\"centroid mean\",\"centroid var\",\"rolloff mean\",\"rolloff var\",\"zero mean\",\n",
" # \"zero var\",\"rms mean\",\"rms var\",\"contrast mean\",\"contrast var\",\"mfcc1 mean\",\"mfcc1 var\",\n",
" # \"mfcc2 mean\",\"mfcc2 var\",\"mfcc3 mean\",\"mfcc3 var\",\"mfcc4 mean\",\"mfcc4 var\",\"mfcc5 mean\",\"mfcc5 var\",\n",
" # \"mfcc6 mean\",\"mfcc6 var\",\"mfcc7 mean\",\"mfcc7 var\",\"mfcc8 mean\",\"mfcc8 var\",\"mfcc9 mean\",\"mfcc9 var\",\n",
" # \"mfcc10 mean\",\"mfcc10 var\",\"mfcc11 mean\",\"mfcc11 var\",\"mfcc12 mean\",\"mfcc12 var\",\"mfcc13 mean\",\"mfcc13 var\"];\n",
"# +[\"chroma_mean_\"+str(i) for i in range(1,13)]+ [\"chroma_var_\"+str(i) for i in range(1,13)]+[\"bpm_second\", \"bm_first\", \"tempo_strength_second\"]\n",
" genre_out={\"blues\":[1,0,0,0,0,0,0,0,0,0],\"classical\":[0,1,0,0,0,0,0,0,0,0],\"country\":[0,0,1,0,0,0,0,0,0,0],\"disco\":[0,0,0,1,0,0,0,0,0,0],\"hiphop\":[0,0,0,0,1,0,0,0,0,0],\"jazz\":[0,0,0,0,0,1,0,0,0,0],\"metal\":[0,0,0,0,0,0,1,0,0,0],\"pop\":[0,0,0,0,0,0,0,1,0,0],\"reggae\":[0,0,0,0,0,0,0,0,1,0],\"rock\":[0,0,0,0,0,0,0,0,0,1]}; \n",
" genre_out_index={\"blues\":0,\"classical\":1,\"country\":2,\"disco\":3,\"hiphop\":4,\"jazz\":5,\"metal\":6,\"pop\":7,\"reggae\":8,\"rock\":9}; \n",
" dataframe=None;\n",
" mfcc_features=5;\n",
" mel_features=5;\n",
" chroma_features=6;\n",
" dir=\"../genres\";\n",
" genre_dir={\"blues\":\"blues\",\"classical\":\"classical\",\"country\":\"country\",\"disco\":\"disco\",\"hiphop\":\"hiphop\",\"jazz\":\"jazz\",\"metal\":\"metal\",\"pop\":\"pop\",\"reggae\":\"reggae\",\"rock\":\"rock\"};\n",
" def __init__(self):\n",
" self.dataframe = pandas.DataFrame(columns=self.columns);\n",
" print(\"Colums\",len(self.columns))\n",
" pass;\n",
"\n",
" def extractTimberalFeatures(self,genre,audio_number,filename):\n",
" features=[];\n",
" pp=PreProcessing(filename); \n",
" centroid=pp.getCentroid()[0];\n",
" rolloff=pp.getSpectralRolloff()[0]; \n",
" zero=pp.getZeroCrossing()[0]; \n",
" contrast=pp.getSpectralContrast()[0]; \n",
" rms=pp.getRMS()[0];\n",
" mfcc=pp.getMFCC();\n",
" mel=pp.getMelSpec();\n",
" chroma = pp.getChroma();\n",
" rhythm = pp.getRhythm_features();\n",
" \n",
" features.append(audio_number);\n",
" features.append(genre); \n",
" features.append(self.genre_out_index[genre]); \n",
" features.append(self.genre_out[genre]);\n",
" features.append(centroid.mean());\n",
"# features.append(centroid.var()); \n",
" features.append(rolloff.mean());\n",
"# features.append(rolloff.var()); \n",
" features.append(zero.mean());\n",
"# features.append(zero.var()); \n",
" features.append(rms.mean());\n",
" features.append(rms.var()); \n",
" features.append(contrast.mean());\n",
" features.append(contrast.var()); \n",
" for i in range(self.mfcc_features):\n",
" features.append(mfcc[i].mean());\n",
" features.append(mfcc[i].var()); \n",
" for i in range(self.mel_features):\n",
" features.append(mel[i].mean());\n",
" features.append(mel[i].var());\n",
" for i in range(self.chroma_features):\n",
" features.append(chroma[i].mean());\n",
" features.append(chroma[i].var());\n",
" #features.append(np.mean(chroma,axis=1).reshape(1,12));\n",
" #features.append(np.var(chroma,axis=1).reshape(1,12));\n",
" #print(np.mean(chroma,axis=1).shape);\n",
" #print(\"Df\",self.dataframe.size)\n",
" #print(\"Features\",np.array(features).shape)\n",
" #print(rhythm.shape)\n",
" for i in range(3):\n",
" features.append(rhythm[i]);\n",
" self.dataframe.loc[self.dataframe.size]=features;\n",
" \n",
" def saveDataFrame(self):\n",
" filename=\"audiofeatures_numpy_matrix\";\n",
" arr=self.dataframe.as_matrix(columns=None);\n",
" np.save(filename,arr); \n",
" self.dataframe.to_csv(\"audiofeatures.csv\",sep=\",\")\n",
" print(\"Written successfully..\");\n",
" pass;\n",
" \n",
" def extractFeatures(self):\n",
" print(\"Extracting Features...\");\n",
" percent_completed=0; \n",
" for k in self.genre_dir: \n",
" genre=k;\n",
" print(\"-------------------[\"+genre+\"]-----------------------\")\n",
" for i in range(100):\n",
" audio_number=\"%0.5d\"%i;\n",
" filename=self.dir+\"/\"+self.genre_dir[genre]+\"/\"+self.genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
" self.extractTimberalFeatures(genre,audio_number,filename); \n",
" percent_completed=i;\n",
" if(percent_completed%10==0):\n",
" print(\"Percent completed:\",percent_completed); \n",
" self.saveDataFrame();\n",
" print(\"Extraction done\");\n",
" pass;\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Colums 46\n",
"Extracting Features...\n",
"-------------------[blues]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[classical]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[country]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[disco]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[hiphop]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[jazz]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[metal]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[pop]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[reggae]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n",
"-------------------[rock]-----------------------\n",
"Percent completed: 0\n",
"Percent completed: 10\n",
"Percent completed: 20\n",
"Percent completed: 30\n",
"Percent completed: 40\n",
"Percent completed: 50\n",
"Percent completed: 60\n",
"Percent completed: 70\n",
"Percent completed: 80\n",
"Percent completed: 90\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:72: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Written successfully..\n",
"Extraction done\n"
]
},
{
"data": {
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" <th></th>\n",
" <th>id</th>\n",
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" <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]</td>\n",
" <td>2009.139574</td>\n",
" <td>4254.785667</td>\n",
" <td>0.089293</td>\n",
" <td>0.079451</td>\n",
" <td>0.000346</td>\n",
" <td>29.623115</td>\n",
" <td>...</td>\n",
" <td>0.084771</td>\n",
" <td>0.406211</td>\n",
" <td>0.035509</td>\n",
" <td>0.330961</td>\n",
" <td>0.093065</td>\n",
" <td>0.217626</td>\n",
" <td>0.026933</td>\n",
" <td>198.768029</td>\n",
" <td>215.332031</td>\n",
" <td>0.775188</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45816</td>\n",
" <td>00096</td>\n",
" <td>rock</td>\n",
" <td>9</td>\n",
" <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]</td>\n",
" <td>2005.279224</td>\n",
" <td>4144.849484</td>\n",
" <td>0.097594</td>\n",
" <td>0.076443</td>\n",
" <td>0.000590</td>\n",
" <td>29.228484</td>\n",
" <td>...</td>\n",
" <td>0.055166</td>\n",
" <td>0.347186</td>\n",
" <td>0.037476</td>\n",
" <td>0.562132</td>\n",
" <td>0.088365</td>\n",
" <td>0.298203</td>\n",
" <td>0.030091</td>\n",
" <td>78.302557</td>\n",
" <td>234.907670</td>\n",
" <td>0.745270</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45862</td>\n",
" <td>00097</td>\n",
" <td>rock</td>\n",
" <td>9</td>\n",
" <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]</td>\n",
" <td>2076.527225</td>\n",
" <td>4029.196153</td>\n",
" <td>0.121735</td>\n",
" <td>0.081619</td>\n",
" <td>0.000322</td>\n",
" <td>28.829981</td>\n",
" <td>...</td>\n",
" <td>0.097083</td>\n",
" <td>0.409299</td>\n",
" <td>0.044416</td>\n",
" <td>0.457064</td>\n",
" <td>0.087882</td>\n",
" <td>0.341546</td>\n",
" <td>0.039950</td>\n",
" <td>123.046875</td>\n",
" <td>258.398438</td>\n",
" <td>0.787496</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45908</td>\n",
" <td>00098</td>\n",
" <td>rock</td>\n",
" <td>9</td>\n",
" <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]</td>\n",
" <td>1398.990121</td>\n",
" <td>3014.656784</td>\n",
" <td>0.048738</td>\n",
" <td>0.083942</td>\n",
" <td>0.001211</td>\n",
" <td>28.549530</td>\n",
" <td>...</td>\n",
" <td>0.066175</td>\n",
" <td>0.226563</td>\n",
" <td>0.040740</td>\n",
" <td>0.334850</td>\n",
" <td>0.086914</td>\n",
" <td>0.379064</td>\n",
" <td>0.116438</td>\n",
" <td>234.907670</td>\n",
" <td>215.332031</td>\n",
" <td>0.669523</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45954</td>\n",
" <td>00099</td>\n",
" <td>rock</td>\n",
" <td>9</td>\n",
" <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]</td>\n",
" <td>1608.559699</td>\n",
" <td>3244.653093</td>\n",
" <td>0.076161</td>\n",
" <td>0.054475</td>\n",
" <td>0.000337</td>\n",
" <td>29.670431</td>\n",
" <td>...</td>\n",
" <td>0.026997</td>\n",
" <td>0.422365</td>\n",
" <td>0.063259</td>\n",
" <td>0.672558</td>\n",
" <td>0.127945</td>\n",
" <td>0.428266</td>\n",
" <td>0.070846</td>\n",
" <td>258.398438</td>\n",
" <td>129.199219</td>\n",
" <td>0.638906</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1000 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" id type y_index y centroid mean \\\n",
"0 00000 blues 0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0] 1784.808293 \n",
"46 00001 blues 0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0] 1529.004413 \n",
"92 00002 blues 0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0] 1552.846406 \n",
"138 00003 blues 0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0] 1070.101734 \n",
"184 00004 blues 0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0] 1836.253062 \n",
"... ... ... ... ... ... \n",
"45770 00095 rock 9 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 2009.139574 \n",
"45816 00096 rock 9 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 2005.279224 \n",
"45862 00097 rock 9 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 2076.527225 \n",
"45908 00098 rock 9 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 1398.990121 \n",
"45954 00099 rock 9 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 1608.559699 \n",
"\n",
" rolloff mean zero mean rms mean rms var contrast mean ... \\\n",
"0 3808.046818 0.083024 0.130224 0.002833 15.995900 ... \n",
"46 3545.433548 0.055998 0.095860 0.002375 15.976383 ... \n",
"92 3041.990598 0.076251 0.175608 0.002753 26.343662 ... \n",
"138 2185.494924 0.033294 0.141386 0.006324 29.793587 ... \n",
"184 3582.540766 0.101533 0.091636 0.002304 17.063747 ... \n",
"... ... ... ... ... ... ... \n",
"45770 4254.785667 0.089293 0.079451 0.000346 29.623115 ... \n",
"45816 4144.849484 0.097594 0.076443 0.000590 29.228484 ... \n",
"45862 4029.196153 0.121735 0.081619 0.000322 28.829981 ... \n",
"45908 3014.656784 0.048738 0.083942 0.001211 28.549530 ... \n",
"45954 3244.653093 0.076161 0.054475 0.000337 29.670431 ... \n",
"\n",
" chroma_mean_6 chroma_var_1 chroma_var_2 chroma_var_3 chroma_var_4 \\\n",
"0 0.100146 0.244700 0.044746 0.247899 0.065782 \n",
"46 0.095222 0.211930 0.052394 0.201273 0.043756 \n",
"92 0.073941 0.408669 0.073894 0.547632 0.122644 \n",
"138 0.106601 0.547292 0.092144 0.657226 0.119275 \n",
"184 0.096787 0.255970 0.075524 0.193062 0.032114 \n",
"... ... ... ... ... ... \n",
"45770 0.084771 0.406211 0.035509 0.330961 0.093065 \n",
"45816 0.055166 0.347186 0.037476 0.562132 0.088365 \n",
"45862 0.097083 0.409299 0.044416 0.457064 0.087882 \n",
"45908 0.066175 0.226563 0.040740 0.334850 0.086914 \n",
"45954 0.026997 0.422365 0.063259 0.672558 0.127945 \n",
"\n",
" chroma_var_5 chroma_var_6 bpm_second bm_first \\\n",
"0 0.335636 0.083699 129.199219 123.046875 \n",
"46 0.331534 0.096766 69.837416 66.256010 \n",
"92 0.322317 0.055691 78.302557 151.999081 \n",
"138 0.511363 0.092011 64.599609 63.024009 \n",
"184 0.531243 0.131189 66.256010 129.199219 \n",
"... ... ... ... ... \n",
"45770 0.217626 0.026933 198.768029 215.332031 \n",
"45816 0.298203 0.030091 78.302557 234.907670 \n",
"45862 0.341546 0.039950 123.046875 258.398438 \n",
"45908 0.379064 0.116438 234.907670 215.332031 \n",
"45954 0.428266 0.070846 258.398438 129.199219 \n",
"\n",
" tempo_strength_second \n",
"0 0.697285 \n",
"46 0.535636 \n",
"92 0.604331 \n",
"138 0.547889 \n",
"184 0.544492 \n",
"... ... \n",
"45770 0.775188 \n",
"45816 0.745270 \n",
"45862 0.787496 \n",
"45908 0.669523 \n",
"45954 0.638906 \n",
"\n",
"[1000 rows x 46 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p1=ProcessDataset();\n",
"p1.extractFeatures();\n",
"p1.dataframe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"dir=\"../genres\";\n",
"genre_dir={\"blues\":\"blues\",\"classical\":\"classical\",\"country\":\"country\",\"disco\":\"disco\",\"hiphop\":\"hiphop\",\"jazz\":\"jazz\",\"metal\":\"metal\",\"pop\":\"pop\",\"reggae\":\"reggae\",\"rock\":\"rock\"};"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1025"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genre=\"disco\"\n",
"\n",
"audio_number=\"%0.5d\"%0;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"\n",
"pp=PreProcessing(filename); \n",
"s,p=pp.getSpectrogram();\n",
"len(s)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1290"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genre=\"disco\"\n",
"audio_number=\"%0.5d\"%0;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"\n",
"pp=PreProcessing(filename); \n",
"a=librosa.feature.melspectrogram(y=pp.y, sr=pp.sr,n_mels=10)\n",
"len(a[0])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Colums 46\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>type</th>\n",
" <th>y_index</th>\n",
" <th>y</th>\n",
" <th>centroid mean</th>\n",
" <th>rolloff mean</th>\n",
" <th>zero mean</th>\n",
" <th>rms mean</th>\n",
" <th>rms var</th>\n",
" <th>contrast mean</th>\n",
" <th>...</th>\n",
" <th>chroma_mean_6</th>\n",
" <th>chroma_var_1</th>\n",
" <th>chroma_var_2</th>\n",
" <th>chroma_var_3</th>\n",
" <th>chroma_var_4</th>\n",
" <th>chroma_var_5</th>\n",
" <th>chroma_var_6</th>\n",
" <th>bpm_second</th>\n",
" <th>bm_first</th>\n",
" <th>tempo_strength_second</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>00098</td>\n",
" <td>disco</td>\n",
" <td>3</td>\n",
" <td>[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
" <td>2417.547068</td>\n",
" <td>5384.369095</td>\n",
" <td>0.112626</td>\n",
" <td>0.122804</td>\n",
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" <td>24.266525</td>\n",
" <td>...</td>\n",
" <td>0.078891</td>\n",
" <td>0.428091</td>\n",
" <td>0.064880</td>\n",
" <td>0.515520</td>\n",
" <td>0.116679</td>\n",
" <td>0.484060</td>\n",
" <td>0.057560</td>\n",
" <td>161.499023</td>\n",
" <td>61.523438</td>\n",
" <td>0.767122</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>00099</td>\n",
" <td>disco</td>\n",
" <td>3</td>\n",
" <td>[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
" <td>2417.547068</td>\n",
" <td>5384.369095</td>\n",
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" <td>0.122804</td>\n",
" <td>0.001554</td>\n",
" <td>24.266525</td>\n",
" <td>...</td>\n",
" <td>0.078891</td>\n",
" <td>0.428091</td>\n",
" <td>0.064880</td>\n",
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" <td>0.484060</td>\n",
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" <td>161.499023</td>\n",
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" <td>0.767122</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92</td>\n",
" <td>00000</td>\n",
" <td>disco</td>\n",
" <td>3</td>\n",
" <td>[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]</td>\n",
" <td>2295.772581</td>\n",
" <td>4884.139433</td>\n",
" <td>0.096195</td>\n",
" <td>0.142213</td>\n",
" <td>0.004145</td>\n",
" <td>18.022094</td>\n",
" <td>...</td>\n",
" <td>0.091744</td>\n",
" <td>0.433943</td>\n",
" <td>0.064803</td>\n",
" <td>0.492812</td>\n",
" <td>0.125874</td>\n",
" <td>0.481689</td>\n",
" <td>0.089429</td>\n",
" <td>57.421875</td>\n",
" <td>112.347147</td>\n",
" <td>0.708882</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" id type y_index y centroid mean \\\n",
"0 00098 disco 3 [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] 2417.547068 \n",
"46 00099 disco 3 [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] 2417.547068 \n",
"92 00000 disco 3 [0, 0, 0, 1, 0, 0, 0, 0, 0, 0] 2295.772581 \n",
"\n",
" rolloff mean zero mean rms mean rms var contrast mean ... \\\n",
"0 5384.369095 0.112626 0.122804 0.001554 24.266525 ... \n",
"46 5384.369095 0.112626 0.122804 0.001554 24.266525 ... \n",
"92 4884.139433 0.096195 0.142213 0.004145 18.022094 ... \n",
"\n",
" chroma_mean_6 chroma_var_1 chroma_var_2 chroma_var_3 chroma_var_4 \\\n",
"0 0.078891 0.428091 0.064880 0.515520 0.116679 \n",
"46 0.078891 0.428091 0.064880 0.515520 0.116679 \n",
"92 0.091744 0.433943 0.064803 0.492812 0.125874 \n",
"\n",
" chroma_var_5 chroma_var_6 bpm_second bm_first tempo_strength_second \n",
"0 0.484060 0.057560 161.499023 61.523438 0.767122 \n",
"46 0.484060 0.057560 161.499023 61.523438 0.767122 \n",
"92 0.481689 0.089429 57.421875 112.347147 0.708882 \n",
"\n",
"[3 rows x 46 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genre=\"disco\"\n",
"pds=ProcessDataset();\n",
"\n",
"audio_number=\"%0.5d\"%98;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"pds.extractTimberalFeatures(genre,audio_number,filename);\n",
"\n",
"audio_number=\"%0.5d\"%99;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"pds.extractTimberalFeatures(genre,audio_number,filename);\n",
"\n",
"audio_number=\"%0.5d\"%0;\n",
"filename=dir+\"/\"+genre_dir[genre]+\"/\"+genre_dir[genre]+\".\"+audio_number+\".wav\"; \n",
"pds.extractTimberalFeatures(genre,audio_number,filename);\n",
"\n",
"#pds.extractFeatures();\n",
"pds.dataframe"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def partitionDataFrame(df,ratio):\n",
" df = df.sample(frac=1).reset_index(drop=True)#shuffling rows\n",
" df = df.sample(frac=1).reset_index(drop=True)#again shuffling\n",
" size=df[\"id\"].count();\n",
" limit=int(ratio*size);\n",
" train_ds=df.loc[0:limit]; \n",
" test_ds=df.loc[limit:size];\n",
" train_ds.to_csv(\"train.csv\",sep=\",\");\n",
" test_ds.to_csv(\"test.csv\",sep=\",\");\n",
" print(\"Partitioning done\");"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Partitioning done\n"
]
}
],
"source": [
"df=readCSVFile(\"audiofeatures.csv\");\n",
"partitionDataFrame(df,0.9);\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
code/readme.txt 0 → 100644
View file @ 0e872aaf
This code is submitted as a part of the Project in the course CS725 in the autumn semester 2019.
The GTZAN dataset(with the name: genres) is required to be kept at the location "../code" . The link to download the dataset is :
http://marsyas.info/downloads/datasets.html
(Approximately 1.2GB)
To run :
Use jupyter notebook.
First run the "preprocess.ipynb", then "trainNtest.ipynb"
Team Details:
193050003 : Suraj Kumar
193050067 : Yadnyesh Patil
193059002 : Rajershi Gupta
193050022 : Aman Kumar Singh
\ No newline at end of file
code/test.csv 0 → 100644
View file @ 0e872aaf
,Unnamed: 0,id,type,y_index,y,centroid mean,rolloff mean,zero mean,rms mean,rms var,contrast mean,contrast var,mfcc1 mean,mfcc1 var,mfcc2 mean,mfcc2 var,mfcc3 mean,mfcc3 var,mfcc4 mean,mfcc4 var,mfcc5 mean,mfcc5 var,mel1 mean,mel1 var,mel2 mean,mel2 var,mel3 mean,mel3 var,mel4 mean,mel4 var,mel5 mean,mel5 var,chroma_mean_1,chroma_mean_2,chroma_mean_3,chroma_mean_4,chroma_mean_5,chroma_mean_6,chroma_var_1,chroma_var_2,chroma_var_3,chroma_var_4,chroma_var_5,chroma_var_6,bpm_second,bm_first,tempo_strength_second
900,32476,6,pop,7,"[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]",2124.8964478867483,4978.025867550872,0.06762089692344961,0.09396819025278093,0.001463073189370334,23.45123500094369,26.480354999476987,-169.18728637695312,4911.0078125,94.65843963623048,579.417236328125,24.54653549194336,352.8388977050781,27.451528549194336,180.11016845703125,11.634444236755373,62.0421371459961,10.076539993286133,82.4892807006836,1.0731030702590942,2.263891696929932,1.3365746736526491,7.020498752593994,0.3260074257850647,0.4545958042144776,0.1829150766134262,0.07699296623468399,0.5539796948432922,0.12931060791015625,0.3808411657810211,0.06823572516441345,0.3234788179397583,0.06779558956623077,0.2583819627761841,0.04783359542489052,0.27921700477600103,0.049788329750299454,0.35297074913978577,0.1194651499390602,61.5234375,184.5703125,0.7486772208367113
901,10258,23,country,2,"[0, 0, 1, 0, 0, 0, 0, 0, 0, 0]",1496.6587872379525,3056.062295603197,0.03550599563953488,0.03877818211913109,0.00046465219929814333,26.927971057556555,27.269813692003346,-353.2105712890625,6483.01025390625,110.63567352294922,801.042236328125,32.833740234375,701.8229370117188,24.165679931640625,275.96588134765625,18.756874084472656,232.31011962890625,2.354298591613769,7.2848472595214835,0.4918922185897827,1.9117369651794436,0.1708715409040451,0.5034690499305725,0.06651458889245987,0.12720222771167755,0.03676969558000565,0.06028443574905396,0.3056666254997253,0.10488881915807724,0.2293792963027954,0.06093376874923706,0.2528017461299896,0.10385967046022417,0.2468795478343964,0.07019539177417755,0.3372190296649933,0.0728430449962616,0.5535060167312622,0.17295707762241366,172.265625,80.74951171875,0.5242828427676084
902,26864,84,jazz,5,"[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]",2872.304374690372,6132.990098110465,0.1181250757025194,0.051622334867715836,0.00046380978892557323,21.310290169948487,23.129727509921747,-178.22836303710938,2495.433837890625,61.9981460571289,369.87396240234375,1.4360841512680054,320.8524169921875,36.07133483886719,108.30149841308594,7.565858840942383,91.6313247680664,1.7936381101608276,2.211169958114624,1.021234154701233,1.881957769393921,0.5251045823097229,0.8102635741233826,0.4186612367630005,0.9022169709205629,0.2385928630828857,0.1809653043746948,0.47040963172912603,0.11741020530462265,0.4164496958255768,0.06510397791862488,0.4475269019603729,0.09835229068994522,0.3864865899085999,0.07085903733968735,0.3669869303703308,0.06634718179702759,0.3298816680908203,0.06687972694635391,63.024009146341456,123.046875,0.5968846339987611
903,23460,10,jazz,5,"[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]",828.5158499128612,1552.1349813771806,0.03306686046511628,0.06413640826940536,0.0016535212052986026,24.240456913794173,20.98650433260248,-318.7840576171875,11668.16015625,162.79043579101562,553.2947387695311,-16.419504165649414,2928.466064453125,39.10525131225586,268.85720825195307,31.488971710205078,199.7197265625,6.1772727966308585,42.44692993164063,0.8561108708381653,2.2517194747924805,0.2356383502483368,0.15707871317863464,0.7284728288650513,1.6824476718902588,0.6643171310424805,1.8585070371627808,0.2653049230575561,0.08573822677135468,0.2647178471088409,0.04678025469183922,0.4398587942123413,0.12813591957092285,0.3810545802116394,0.10429853200912476,0.3588166832923889,0.07918273657560349,0.2461797147989273,0.058385353535413735,161.4990234375,117.45383522727272,0.3903696591899212
904,36662,97,pop,7,"[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]",3512.2924402843737,7589.869867369186,0.13742656855620156,0.2040589302778244,0.01963871531188488,20.57897473758914,26.806870539163498,-125.5443344116211,5416.26904296875,51.14413070678711,1156.39501953125,38.53379821777344,1129.285888671875,14.475336074829102,492.0675964355469,16.201366424560547,272.51229858398443,33.46435546875,1876.7352294921875,2.9598603248596187,21.363374710083008,1.3225435018539429,3.830956220626831,0.7698804140090942,2.4658844470977783,0.6602984070777893,2.6089131832122803,0.4468372762203217,0.08848461508750917,0.42452239990234375,0.07063424587249756,0.4915040135383606,0.11016535758972168,0.4218857884407044,0.0772789865732193,0.4403286278247833,0.08156415075063705,0.4220324754714966,0.07972027361392975,198.76802884615384,99.38401442307692,0.6982929946619664
905,38226,31,reggae,8,"[0, 0, 0, 0, 0, 0, 0, 0, 1, 0]",1576.9127914898706,3538.197390534157,0.05848171027131782,0.09219224005937576,0.0018863707082346077,27.603891250717115,30.60474304308737,-226.328857421875,8562.89453125,103.4830322265625,646.3579711914062,0.2701510787010193,985.3954467773438,44.01379776000977,353.2588806152344,10.727282524108887,517.4627075195311,7.970878601074219,105.83350372314452,0.5202570557594299,1.2595683336257937,0.4642479717731476,2.1668448448181152,0.5767558813095093,2.694655179977417,0.16260527074337006,0.13777032494544986,0.6478978991508484,0.11289604008197784,0.5218036770820618,0.04802216589450836,0.5439463853836061,0.0728226751089096,0.397508442401886,0.048051558434963226,0.4156799912452698,0.08691543340682982,0.3967621922492981,0.06444734334945679,52.734375,80.74951171875,0.6603538654416371
906,28474,19,metal,6,"[0, 0, 0, 0, 0, 0, 1, 0, 0, 0]",2700.647462528509,5386.973110465116,0.16150337633236433,0.10085272043943404,0.0009691770537756382,15.183970319822269,16.071322404985832,-89.31684875488281,3157.326416015625,82.32291412353516,376.2969665527344,-23.261905670166016,210.69859313964844,68.09683990478516,139.68887329101562,-13.098339080810547,83.45713806152344,9.73676300048828,60.50719451904297,1.5674368143081665,2.599796772003174,0.8706703186035156,1.8138788938522337,0.5419857501983643,1.1713584661483765,0.6229020357131958,0.9303933382034302,0.4593130052089691,0.05250740796327591,0.4582765698432922,0.05538154393434525,0.4939363300800324,0.05201875045895577,0.5468605160713196,0.06472524255514145,0.5874320268630981,0.05918702110648155,0.5678079724311829,0.051189221441745765,112.34714673913044,107.666015625,0.7022088636886886
907,44942,77,rock,9,"[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]",2188.620574204777,4994.117346475291,0.1062935289486434,0.12876585125923154,0.0019139201613143086,17.735840760236407,23.671518133833818,-103.8831024169922,2909.5634765625,101.88799285888672,351.39215087890625,-10.35607624053955,427.8530883789063,43.67078018188477,158.20645141601562,-11.544981002807615,134.73138427734378,16.080528259277347,195.6978454589844,3.314534902572632,19.18746566772461,3.066410779953003,23.01833915710449,1.5794744491577148,9.026198387145996,0.7742032408714294,1.8316625356674197,0.3886460065841675,0.06081227585673332,0.4260536730289459,0.07736101746559143,0.3877261579036713,0.05323388054966927,0.5444072484970093,0.09776517003774644,0.4789472222328186,0.0640333890914917,0.4867457151412964,0.07007323205471039,215.33203125,53.8330078125,0.6763846506054311
908,27462,97,jazz,5,"[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]",1175.1869624736114,2340.993027797965,0.03987251695736434,0.021218478679656986,0.00018388759053777903,20.76920795654405,28.346583215033675,-413.6007690429688,2118.906494140625,118.30582427978516,534.1683349609375,31.173622131347656,807.3428955078125,40.15095520019531,169.4828338623047,13.11996078491211,148.06040954589844,0.6402610540390015,0.8494895696640015,0.23275141417980194,0.10915809869766237,0.04983975365757942,0.012028771452605723,0.020265540108084682,0.0031494677532464266,0.0065352190285921105,0.000774556421674788,0.1757855862379074,0.05391702428460121,0.23242759704589844,0.11619378626346588,0.1922404915094376,0.07096368074417114,0.22960011661052704,0.1244485080242157,0.1949043869972229,0.07140867412090303,0.1989830732345581,0.08264843374490738,54.97839095744681,198.76802884615384,0.5054011246925231
909,1288,28,blues,0,"[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]",1115.660646203261,2289.3968023255816,0.047986312984496134,0.0997576341032982,0.0041530714370310315,15.088748278749025,20.099145893752166,-248.9816131591797,4978.76708984375,139.06259155273438,399.2952575683594,-16.255664825439453,1165.5279541015625,57.04351425170898,328.7342529296875,20.20447540283203,158.43019104003906,14.673212051391602,235.8189849853516,4.968308448791504,109.48518371582031,1.8627793788909912,32.892765045166016,3.7309861183166495,338.3106384277344,2.3739287853240967,105.22175598144531,0.2905121147632599,0.08497443050146103,0.2203947603702545,0.06670602411031723,0.25772103667259216,0.10017769038677216,0.17737622559070587,0.051497992128133774,0.21067187190055847,0.06919437646865845,0.3078413009643555,0.11358974128961564,103.359375,198.76802884615384,0.6619229836192929
910,34316,46,pop,7,"[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]",2836.9072471508625,6483.33865143532,0.1220203488372093,0.20339107513427732,0.005568280816078186,23.29691699094829,36.64697108801462,-72.28729248046875,5425.70556640625,81.3790054321289,1087.34521484375,16.795759201049805,1011.3702392578124,8.66297721862793,405.15557861328125,14.12635612487793,247.68638610839844,38.457313537597656,1560.0589599609375,7.368675231933594,41.892845153808594,5.3872647285461435,40.39888000488281,3.329366445541382,21.956762313842773,2.4446187019348145,17.508920669555664,0.4276010990142822,0.11234140396118164,0.405032217502594,0.09414894133806227,0.3616273403167725,0.07074276357889175,0.4078534841537476,0.08966249227523804,0.39189064502716064,0.07319419831037521,0.4999436438083649,0.10475707799196243,172.265625,117.45383522727272,0.6933703776436985
911,7452,62,classical,1,"[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]",1475.154232379175,2810.450240734011,0.0740226804748062,0.03899567574262619,0.001311509171500802,29.364362476280032,20.377147783883075,-305.31573486328125,19565.29296875,135.90338134765622,1056.6259765625,-11.741782188415527,461.6817932128906,18.203176498413086,108.4419937133789,10.39816188812256,148.40110778808594,1.7783067226409912,10.464824676513672,1.2222553491592407,5.687466144561768,0.8059856295585632,4.1435227394103995,0.7535573840141296,3.5402824878692627,0.4864453673362732,2.5866858959198,0.4345586597919464,0.13452929258346558,0.2599126398563385,0.036280617117881775,0.33320632576942444,0.10639648139476776,0.21809355914592746,0.059493675827980035,0.24328374862670896,0.0395021103322506,0.4542883634567261,0.1273147016763687,89.10290948275862,172.265625,0.6881952436255491
912,34960,60,pop,7,"[0, 0, 0, 0, 0, 0, 0, 1, 0, 0]",3498.7501500586377,7665.945505541425,0.13586028343023254,0.2740567922592163,0.015130908228456976,18.8464407209014,33.185277498093,-46.35695266723633,4335.12109375,55.8851318359375,946.797119140625,26.181718826293945,559.8524780273439,16.538450241088867,245.9687957763672,7.1011924743652335,213.04937744140625,48.87765502929688,2383.119873046875,18.28315544128418,551.5888061523439,10.337197303771973,250.5611724853516,3.63270115852356,74.05972290039062,2.3230252265930176,31.85644340515137,0.4983810186386109,0.11939310282468794,0.41653650999069214,0.08934042602777481,0.36059141159057617,0.07195419073104857,0.4528666734695434,0.12284477055072784,0.4139856696128845,0.11300843954086305,0.4173034727573395,0.1373659074306488,64.599609375,78.30255681818181,0.5426753892481895
913,37490,15,reggae,8,"[0, 0, 0, 0, 0, 0, 0, 0, 1, 0]",1924.8072253525927,4496.266351744186,0.06827988735465118,0.13209307193756106,0.0029715970158576965,18.81330221266457,14.526086643787167,-157.55178833007812,3851.56982421875,109.80972290039062,845.4383544921875,8.477041244506836,521.0712280273439,41.79124069213867,275.97836303710943,5.096587181091309,201.5833282470703,20.843297958374023,412.5670471191406,3.4247827529907227,39.3358154296875,1.061726450920105,6.051980972290039,0.5782095789909363,3.5075695514678955,0.3721153140068054,0.5855040550231934,0.32880595326423645,0.08196521550416946,0.34560859203338623,0.0795675590634346,0.4473603963851929,0.12525300681591034,0.4189053177833557,0.11476296186447145,0.329593300819397,0.061430051922798164,0.41550663113594055,0.11702095717191695,123.046875,63.024009146341456,0.708137446101194
914,20240,40,hiphop,4,"[0, 0, 0, 0, 1, 0, 0, 0, 0, 0]",2429.75140581165,5271.84558957122,0.10139179081879847,0.13064056634902954,0.010919202119112015,25.033259387757663,29.477530020905032,-185.4112091064453,9968.783203125,96.80635833740234,2127.042724609375,44.62500762939453,740.9881591796875,13.899332046508787,308.9228210449219,5.999122619628906,247.4444732666016,20.746788024902344,1739.03955078125,6.507495880126952,34.649253845214844,2.4012997150421143,27.558218002319336,1.6932607889175415,21.157352447509766,0.4359277486801148,1.1548539400100708,0.24397164583206174,0.041726909577846534,0.41622447967529297,0.12836946547031405,0.3342703878879547,0.0617963932454586,0.5283083915710449,0.13816528022289276,0.324966698884964,0.06805851310491562,0.3364435136318207,0.0833204835653305,78.30255681818181,80.74951171875,0.5858607773135753
915,10488,28,country,2,"[0, 0, 1, 0, 0, 0, 0, 0, 0, 0]",1472.8321115131307,2922.9069767441856,0.05978682170542635,0.09421531856060028,0.0009501975728198887,27.65887566741971,36.63772420098112,-169.12982177734375,1607.350341796875,132.87136840820312,334.7182312011719,-13.480005264282227,267.8920288085937,31.19780158996582,161.5392608642578,6.3390254974365225,162.08226013183594,10.843610763549805,59.78014755249024,4.5686869621276855,33.474205017089844,1.3451681137084959,4.523875236511231,0.5695623159408569,0.935800075531006,0.4983722567558289,0.72385573387146,0.2249640375375748,0.06489972025156021,0.3447352647781372,0.08728556334972383,0.41399285197258,0.10739099979400636,0.27135857939720154,0.05544046312570572,0.3381713926792145,0.11281485110521315,0.3250252306461334,0.07661531120538713,172.265625,89.10290948275862,0.7201640190962383
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code/train.csv 0 → 100644
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code/trainNtest.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas;\n",
"import numpy as np;\n",
"import ast;\n",
"import sklearn.tree as tree;\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.multiclass import OneVsRestClassifier\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn import datasets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def readCSVFile(file):\n",
" data=pandas.read_csv(file,\",\",header=0, na_values='?', skipinitialspace=True);\n",
" return data;\n",
" pass;\n",
"def readTrainData(dataset): \n",
" return dataset.iloc[:,6:], dataset.iloc[:,4:5].astype(int),dataset.iloc[:,5:6];\n",
" pass;\n",
"\n",
"def readTestData(dataset): \n",
" return dataset.iloc[:,6:], dataset.iloc[:,4:5].astype(int),dataset.iloc[:,5:6];\n",
" pass;\n",
"\n",
"def normalizePhi(unNormalizedPhi,last_col_bias=False): \n",
" #assuming last column as bias column\n",
" no_of_column=len(unNormalizedPhi[0]);\n",
" phi=np.array(unNormalizedPhi);\n",
" std=phi.std(0);\n",
" mean=phi.mean(0); \n",
" #std[no_of_column-1]=1;\n",
" #mean[no_of_column-1]=0;\n",
" #phi_normalize=(phi-mean)/std; \n",
" \n",
" max_vector=phi.max(axis=0)\n",
" phi_normalize=phi/max_vector; \n",
" \n",
" return phi_normalize;\n",
" pass;\n",
"\n",
"def categoryToNumber(dataset,categoryList):\n",
" for c in categoryList:\n",
" if (c in dataset): \n",
" dataset[c]=pandas.get_dummies(dataset[c]).values.argmax(1); \n",
" return dataset;\n",
" pass;\n",
" \n",
"\n",
"def handleCategoryData(dataset,categoryList): \n",
" return categoryToNumber(dataset,categoryList)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n",
" 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris = datasets.load_iris()\n",
"X, y = iris.data, iris.target\n",
"OneVsRestClassifier(LinearSVC(random_state=0,max_iter=4000)).fit(X, y).predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" if __name__ == '__main__':\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" # Remove the CWD from sys.path while we load stuff.\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" # This is added back by InteractiveShellApp.init_path()\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:17: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:18: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:19: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n"
]
}
],
"source": [
"dir=\"./\"\n",
"trainFile=dir+\"train.csv\";\n",
"testFile=dir+\"test.csv\";\n",
"trained_dataset=readCSVFile(trainFile);\n",
"test_dataset=readCSVFile(testFile);\n",
"trained_data,trained_y,trained_y_vector=readTrainData(trained_dataset);\n",
"test_data,test_y,test_y_vector=readTestData(test_dataset);\n",
"\n",
"mtx_train =trained_data.as_matrix(columns=None)\n",
"mtx_train_y =trained_y.as_matrix(columns=None)\n",
"mtx_trained_y_vector=trained_y_vector.as_matrix(columns=None);\n",
"\n",
"mtx_train_norm=normalizePhi(mtx_train);\n",
"mtx_train_y=np.array(list((e[0] for e in mtx_train_y)));\n",
"mtx_trained_y_vector=np.array(list((ast.literal_eval(e[0]) for e in mtx_trained_y_vector)));\n",
"\n",
"mtx_test=test_data.as_matrix(columns=None);\n",
"mtx_test_y=test_y.as_matrix(columns=None);\n",
"mtx_test_y_vector=test_y_vector.as_matrix(columns=None);\n",
"\n",
"mtx_test_norm=normalizePhi(mtx_test);\n",
"mtx_test_y=np.array(list((e[0] for e in mtx_test_y)));\n",
"mtx_test_y_vector=np.array(list((ast.literal_eval(e[0]) for e in mtx_test_y_vector)));"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# #trainedModel=OneVsRestClassifier(LinearSVC()).fit(mtx_train, mtx_train_y);\n",
"# trainedModel=LinearSVC(max_iter=12000).fit(mtx_train, mtx_train_y);\n",
"# y_predict=trainedModel.predict(mtx_train);\n",
"# print(y_predict);\n",
"# print(\"done\");"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[9 8 5 8 7 8 6 9 5 0 7 1 7 9 9 5 9 9 9 6 5 6 1 7 1 7 1 1 6 9 9 0 6 9 9 8 9\n",
" 9 9 9 9 5 8 7 6 0 9 1 7 9 0 5 9 9 9 9 7 1 1 5 0 9 7 9 5 6 9 6 9 9 0 9 9 5\n",
" 9 9 9 1 6 1 6 0 9 7 6 6 6 0 1 9 7 7 6 9 1 7 9 9 0 1]\n"
]
}
],
"source": [
"y_predict=trainedModel.predict(mtx_test);\n",
"print(y_predict);"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(901, 42)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/svm/base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" \"the number of iterations.\", ConvergenceWarning)\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n",
"/home/yadnyesh/.local/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
" \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yadnyesh/.local/lib/python3.6/site-packages/ipykernel_launcher.py:35: FutureWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Trained Misclassified Points</th>\n",
" <th>Trained Accuracy</th>\n",
" <th>Test Misclassified Points</th>\n",
" <th>Test Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Decision Tree</td>\n",
" <td>191</td>\n",
" <td>78.8013</td>\n",
" <td>58</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Random Forest</td>\n",
" <td>50</td>\n",
" <td>94.4506</td>\n",
" <td>38</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <td>K-Neighbors</td>\n",
" <td>323</td>\n",
" <td>64.1509</td>\n",
" <td>61</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Logistic Regression</td>\n",
" <td>279</td>\n",
" <td>69.0344</td>\n",
" <td>35</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Support Vector</td>\n",
" <td>433</td>\n",
" <td>51.9423</td>\n",
" <td>49</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Bernoulli NB</td>\n",
" <td>711</td>\n",
" <td>21.0877</td>\n",
" <td>81</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Gaussian NB</td>\n",
" <td>413</td>\n",
" <td>54.162</td>\n",
" <td>48</td>\n",
" <td>52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Trained Misclassified Points Trained Accuracy \\\n",
"Decision Tree 191 78.8013 \n",
"Random Forest 50 94.4506 \n",
"K-Neighbors 323 64.1509 \n",
"Logistic Regression 279 69.0344 \n",
"Support Vector 433 51.9423 \n",
"Bernoulli NB 711 21.0877 \n",
"Gaussian NB 413 54.162 \n",
"\n",
" Test Misclassified Points Test Accuracy \n",
"Decision Tree 58 42 \n",
"Random Forest 38 62 \n",
"K-Neighbors 61 39 \n",
"Logistic Regression 35 65 \n",
"Support Vector 49 51 \n",
"Bernoulli NB 81 19 \n",
"Gaussian NB 48 52 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = trained_data\n",
"print(x.shape)\n",
"y = np.ravel(trained_y)\n",
"y_test=np.ravel(test_y);\n",
"\n",
"index = [\"Decision Tree\", \"Random Forest\", \"K-Neighbors\",\n",
" \"Logistic Regression\", \"Support Vector\", \"Bernoulli NB\", \"Gaussian NB\"]\n",
"columns = [\"Trained Misclassified Points\", \"Trained Accuracy\", \"Test Misclassified Points\", \"Test Accuracy\",]\n",
"modelComparision = pandas.DataFrame(index=index, columns=columns)\n",
"\n",
"dtc = 0, DecisionTreeClassifier(min_samples_split=20)\n",
"rfc = 1, RandomForestClassifier(min_samples_split=10)\n",
"knn = 2, KNeighborsClassifier()\n",
"lgr = 3, LogisticRegression(max_iter=20000)\n",
"svc = 4, LinearSVC(max_iter=15000)\n",
"bnb = 5, BernoulliNB()\n",
"gnb = 6, GaussianNB()\n",
"\n",
"tr_size=len(x.index);\n",
"tt_size=len(y_test);\n",
"for i in lgr, gnb, bnb, dtc, svc, knn, rfc: \n",
" model=i[1].fit(x, y);\n",
" y_pred = model.predict(x)\n",
" tr_misclassifedPoints = (y_pred != y).sum() \n",
" tr_accuracy = ((tr_size - tr_misclassifedPoints)*100) / tr_size;\n",
" \n",
" y_pred = model.predict(test_data)\n",
" tt_misclassifedPoints = (y_pred != y_test).sum() \n",
" tt_accuracy = ((tt_size - tt_misclassifedPoints)*100) / tt_size;\n",
" if(i[0]==0):\n",
" tree.export_graphviz(model,out_file='tree.dot'); \n",
" #print(misclassifedPoints,\":\",size - misclassifedPoints,':',accuracy); \n",
" modelComparision.ix[i[0]] = [tr_misclassifedPoints, tr_accuracy, tt_misclassifedPoints, tt_accuracy,];\n",
" \n",
"print(\"Done\");\n",
"modelComparision"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
code/tree.dot 0 → 100644
View file @ 0e872aaf
digraph Tree {
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2 [label="X[17] <= 1.02\ngini = 0.682\nsamples = 30\nvalue = [0, 7, 1, 0, 1, 15, 1, 1, 2, 2]"] ;
1 -> 2 ;
3 [label="gini = 0.48\nsamples = 10\nvalue = [0, 7, 1, 0, 0, 1, 0, 0, 1, 0]"] ;
2 -> 3 ;
4 [label="X[14] <= 275.982\ngini = 0.49\nsamples = 20\nvalue = [0, 0, 0, 0, 1, 14, 1, 1, 1, 2]"] ;
2 -> 4 ;
5 [label="gini = 0.227\nsamples = 16\nvalue = [0, 0, 0, 0, 0, 14, 0, 1, 0, 1]"] ;
4 -> 5 ;
6 [label="gini = 0.75\nsamples = 4\nvalue = [0, 0, 0, 0, 1, 0, 1, 0, 1, 1]"] ;
4 -> 6 ;
7 [label="X[39] <= 71.833\ngini = 0.136\nsamples = 69\nvalue = [0, 64, 1, 0, 0, 4, 0, 0, 0, 0]"] ;
1 -> 7 ;
8 [label="gini = 0.625\nsamples = 4\nvalue = [0, 1, 1, 0, 0, 2, 0, 0, 0, 0]"] ;
7 -> 8 ;
9 [label="X[4] <= 0.002\ngini = 0.06\nsamples = 65\nvalue = [0, 63, 0, 0, 0, 2, 0, 0, 0, 0]"] ;
7 -> 9 ;
10 [label="X[20] <= 37.19\ngini = 0.031\nsamples = 64\nvalue = [0, 63, 0, 0, 0, 1, 0, 0, 0, 0]"] ;
9 -> 10 ;
11 [label="gini = 0.0\nsamples = 62\nvalue = [0, 62, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
10 -> 11 ;
12 [label="gini = 0.5\nsamples = 2\nvalue = [0, 1, 0, 0, 0, 1, 0, 0, 0, 0]"] ;
10 -> 12 ;
13 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]"] ;
9 -> 13 ;
14 [label="X[4] <= 0.006\ngini = 0.893\nsamples = 802\nvalue = [93, 20, 89, 89, 90, 71, 89, 91, 87, 83]"] ;
0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
15 [label="X[8] <= 915.213\ngini = 0.887\nsamples = 674\nvalue = [86, 20, 89, 84, 44, 70, 87, 33, 78, 83]"] ;
14 -> 15 ;
16 [label="X[13] <= 39.691\ngini = 0.516\nsamples = 68\nvalue = [4, 2, 8, 0, 0, 2, 46, 0, 0, 6]"] ;
15 -> 16 ;
17 [label="gini = 0.597\nsamples = 12\nvalue = [0, 2, 7, 0, 0, 1, 0, 0, 0, 2]"] ;
16 -> 17 ;
18 [label="X[35] <= 0.385\ngini = 0.314\nsamples = 56\nvalue = [4, 0, 1, 0, 0, 1, 46, 0, 0, 4]"] ;
16 -> 18 ;
19 [label="gini = 0.656\nsamples = 8\nvalue = [4, 0, 0, 0, 0, 1, 1, 0, 0, 2]"] ;
18 -> 19 ;
20 [label="X[30] <= 0.041\ngini = 0.119\nsamples = 48\nvalue = [0, 0, 1, 0, 0, 0, 45, 0, 0, 2]"] ;
18 -> 20 ;
21 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]"] ;
20 -> 21 ;
22 [label="X[12] <= 289.309\ngini = 0.081\nsamples = 47\nvalue = [0, 0, 0, 0, 0, 0, 45, 0, 0, 2]"] ;
20 -> 22 ;
23 [label="X[38] <= 0.093\ngini = 0.043\nsamples = 46\nvalue = [0, 0, 0, 0, 0, 0, 45, 0, 0, 1]"] ;
22 -> 23 ;
24 [label="gini = 0.0\nsamples = 44\nvalue = [0, 0, 0, 0, 0, 0, 44, 0, 0, 0]"] ;
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25 [label="gini = 0.5\nsamples = 2\nvalue = [0, 0, 0, 0, 0, 0, 1, 0, 0, 1]"] ;
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26 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]"] ;
22 -> 26 ;
27 [label="X[1] <= 4353.914\ngini = 0.886\nsamples = 606\nvalue = [82, 18, 81, 84, 44, 68, 41, 33, 78, 77]"] ;
15 -> 27 ;
28 [label="X[16] <= 121.452\ngini = 0.842\nsamples = 279\nvalue = [58, 16, 59, 4, 10, 51, 8, 5, 40, 28]"] ;
27 -> 28 ;
29 [label="X[41] <= 0.651\ngini = 0.701\nsamples = 73\nvalue = [4, 12, 2, 2, 0, 36, 7, 0, 1, 9]"] ;
28 -> 29 ;
30 [label="X[13] <= 52.622\ngini = 0.368\nsamples = 42\nvalue = [2, 2, 1, 0, 0, 33, 4, 0, 0, 0]"] ;
29 -> 30 ;
31 [label="X[30] <= 0.027\ngini = 0.199\nsamples = 37\nvalue = [2, 2, 0, 0, 0, 33, 0, 0, 0, 0]"] ;
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32 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
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35 [label="gini = 0.0\nsamples = 32\nvalue = [0, 0, 0, 0, 0, 32, 0, 0, 0, 0]"] ;
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29 -> 37 ;
38 [label="gini = 0.0\nsamples = 10\nvalue = [0, 10, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
37 -> 38 ;
39 [label="X[29] <= 0.375\ngini = 0.753\nsamples = 21\nvalue = [2, 0, 1, 2, 0, 3, 3, 0, 1, 9]"] ;
37 -> 39 ;
40 [label="gini = 0.79\nsamples = 9\nvalue = [2, 0, 1, 1, 0, 3, 1, 0, 1, 0]"] ;
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41 [label="gini = 0.403\nsamples = 12\nvalue = [0, 0, 0, 1, 0, 0, 2, 0, 0, 9]"] ;
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42 [label="X[6] <= 32.971\ngini = 0.802\nsamples = 206\nvalue = [54, 4, 57, 2, 10, 15, 1, 5, 39, 19]"] ;
28 -> 42 ;
43 [label="X[5] <= 20.44\ngini = 0.791\nsamples = 180\nvalue = [51, 4, 56, 2, 9, 14, 1, 5, 19, 19]"] ;
42 -> 43 ;
44 [label="X[26] <= 6.131\ngini = 0.8\nsamples = 65\nvalue = [22, 4, 2, 0, 4, 10, 1, 2, 13, 7]"] ;
43 -> 44 ;
45 [label="X[29] <= 0.28\ngini = 0.821\nsamples = 45\nvalue = [4, 2, 2, 0, 4, 10, 1, 2, 13, 7]"] ;
44 -> 45 ;
46 [label="gini = 0.524\nsamples = 15\nvalue = [2, 1, 1, 0, 1, 10, 0, 0, 0, 0]"] ;
45 -> 46 ;
47 [label="X[39] <= 132.599\ngini = 0.736\nsamples = 30\nvalue = [2, 1, 1, 0, 3, 0, 1, 2, 13, 7]"] ;
45 -> 47 ;
48 [label="X[6] <= 19.811\ngini = 0.789\nsamples = 21\nvalue = [2, 0, 1, 0, 3, 0, 1, 2, 5, 7]"] ;
47 -> 48 ;
49 [label="gini = 0.75\nsamples = 8\nvalue = [2, 0, 0, 0, 3, 0, 1, 1, 1, 0]"] ;
48 -> 49 ;
50 [label="gini = 0.604\nsamples = 13\nvalue = [0, 0, 1, 0, 0, 0, 0, 1, 4, 7]"] ;
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51 [label="gini = 0.198\nsamples = 9\nvalue = [0, 1, 0, 0, 0, 0, 0, 0, 8, 0]"] ;
47 -> 51 ;
52 [label="X[41] <= 0.733\ngini = 0.18\nsamples = 20\nvalue = [18, 2, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
44 -> 52 ;
53 [label="gini = 0.0\nsamples = 18\nvalue = [18, 0, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
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54 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
52 -> 54 ;
55 [label="X[18] <= 341.295\ngini = 0.698\nsamples = 115\nvalue = [29, 0, 54, 2, 5, 4, 0, 3, 6, 12]"] ;
43 -> 55 ;
56 [label="X[20] <= 3.785\ngini = 0.601\nsamples = 77\nvalue = [7, 0, 47, 1, 3, 4, 0, 3, 6, 6]"] ;
55 -> 56 ;
57 [label="gini = 0.745\nsamples = 14\nvalue = [5, 0, 0, 0, 2, 1, 0, 0, 2, 4]"] ;
56 -> 57 ;
58 [label="X[36] <= 0.045\ngini = 0.432\nsamples = 63\nvalue = [2, 0, 47, 1, 1, 3, 0, 3, 4, 2]"] ;
56 -> 58 ;
59 [label="gini = 0.625\nsamples = 4\nvalue = [0, 0, 0, 0, 0, 1, 0, 2, 0, 1]"] ;
58 -> 59 ;
60 [label="X[40] <= 246.653\ngini = 0.357\nsamples = 59\nvalue = [2, 0, 47, 1, 1, 2, 0, 1, 4, 1]"] ;
58 -> 60 ;
61 [label="X[6] <= 15.691\ngini = 0.291\nsamples = 56\nvalue = [2, 0, 47, 1, 1, 2, 0, 0, 2, 1]"] ;
60 -> 61 ;
62 [label="gini = 0.5\nsamples = 2\nvalue = [0, 0, 0, 0, 1, 1, 0, 0, 0, 0]"] ;
61 -> 62 ;
63 [label="X[33] <= 0.418\ngini = 0.239\nsamples = 54\nvalue = [2, 0, 47, 1, 0, 1, 0, 0, 2, 1]"] ;
61 -> 63 ;
64 [label="X[38] <= 0.021\ngini = 0.125\nsamples = 46\nvalue = [0, 0, 43, 0, 0, 1, 0, 0, 1, 1]"] ;
63 -> 64 ;
65 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]"] ;
64 -> 65 ;
66 [label="X[27] <= 0.594\ngini = 0.086\nsamples = 45\nvalue = [0, 0, 43, 0, 0, 1, 0, 0, 1, 0]"] ;
64 -> 66 ;
67 [label="X[29] <= 0.201\ngini = 0.044\nsamples = 44\nvalue = [0, 0, 43, 0, 0, 1, 0, 0, 0, 0]"] ;
66 -> 67 ;
68 [label="gini = 0.5\nsamples = 2\nvalue = [0, 0, 1, 0, 0, 1, 0, 0, 0, 0]"] ;
67 -> 68 ;
69 [label="gini = 0.0\nsamples = 42\nvalue = [0, 0, 42, 0, 0, 0, 0, 0, 0, 0]"] ;
67 -> 69 ;
70 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]"] ;
66 -> 70 ;
71 [label="gini = 0.656\nsamples = 8\nvalue = [2, 0, 4, 1, 0, 0, 0, 0, 1, 0]"] ;
63 -> 71 ;
72 [label="gini = 0.444\nsamples = 3\nvalue = [0, 0, 0, 0, 0, 0, 0, 1, 2, 0]"] ;
60 -> 72 ;
73 [label="X[30] <= 0.047\ngini = 0.602\nsamples = 38\nvalue = [22, 0, 7, 1, 2, 0, 0, 0, 0, 6]"] ;
55 -> 73 ;
74 [label="gini = 0.58\nsamples = 10\nvalue = [2, 0, 6, 0, 1, 0, 0, 0, 0, 1]"] ;
73 -> 74 ;
75 [label="X[19] <= 13.585\ngini = 0.454\nsamples = 28\nvalue = [20, 0, 1, 1, 1, 0, 0, 0, 0, 5]"] ;
73 -> 75 ;
76 [label="X[40] <= 49.331\ngini = 0.288\nsamples = 24\nvalue = [20, 0, 0, 0, 1, 0, 0, 0, 0, 3]"] ;
75 -> 76 ;
77 [label="gini = 0.0\nsamples = 2\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 2]"] ;
76 -> 77 ;
78 [label="X[36] <= 0.125\ngini = 0.169\nsamples = 22\nvalue = [20, 0, 0, 0, 1, 0, 0, 0, 0, 1]"] ;
76 -> 78 ;
79 [label="X[20] <= 4.194\ngini = 0.091\nsamples = 21\nvalue = [20, 0, 0, 0, 1, 0, 0, 0, 0, 0]"] ;
78 -> 79 ;
80 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0]"] ;
79 -> 80 ;
81 [label="gini = 0.0\nsamples = 20\nvalue = [20, 0, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
79 -> 81 ;
82 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]"] ;
78 -> 82 ;
83 [label="gini = 0.625\nsamples = 4\nvalue = [0, 0, 1, 1, 0, 0, 0, 0, 0, 2]"] ;
75 -> 83 ;
84 [label="X[29] <= 0.308\ngini = 0.391\nsamples = 26\nvalue = [3, 0, 1, 0, 1, 1, 0, 0, 20, 0]"] ;
42 -> 84 ;
85 [label="gini = 0.667\nsamples = 6\nvalue = [3, 0, 1, 0, 0, 1, 0, 0, 1, 0]"] ;
84 -> 85 ;
86 [label="X[8] <= 2908.947\ngini = 0.095\nsamples = 20\nvalue = [0, 0, 0, 0, 1, 0, 0, 0, 19, 0]"] ;
84 -> 86 ;
87 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0]"] ;
86 -> 87 ;
88 [label="gini = 0.0\nsamples = 19\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 19, 0]"] ;
86 -> 88 ;
89 [label="X[13] <= 46.914\ngini = 0.863\nsamples = 327\nvalue = [24, 2, 22, 80, 34, 17, 33, 28, 38, 49]"] ;
27 -> 89 ;
90 [label="X[14] <= 127.242\ngini = 0.829\nsamples = 231\nvalue = [4, 2, 19, 71, 21, 17, 3, 28, 33, 33]"] ;
89 -> 90 ;
91 [label="X[11] <= -4.759\ngini = 0.733\nsamples = 36\nvalue = [1, 2, 2, 3, 1, 15, 1, 1, 0, 10]"] ;
90 -> 91 ;
92 [label="gini = 0.71\nsamples = 18\nvalue = [1, 2, 1, 2, 1, 0, 1, 1, 0, 9]"] ;
91 -> 92 ;
93 [label="gini = 0.296\nsamples = 18\nvalue = [0, 0, 1, 1, 0, 15, 0, 0, 0, 1]"] ;
91 -> 93 ;
94 [label="X[2] <= 0.089\ngini = 0.798\nsamples = 195\nvalue = [3, 0, 17, 68, 20, 2, 2, 27, 33, 23]"] ;
90 -> 94 ;
95 [label="X[35] <= 0.279\ngini = 0.711\nsamples = 38\nvalue = [0, 0, 3, 2, 6, 0, 0, 6, 18, 3]"] ;
94 -> 95 ;
96 [label="gini = 0.32\nsamples = 5\nvalue = [0, 0, 0, 0, 0, 0, 0, 4, 0, 1]"] ;
95 -> 96 ;
97 [label="X[22] <= 8.885\ngini = 0.65\nsamples = 33\nvalue = [0, 0, 3, 2, 6, 0, 0, 2, 18, 2]"] ;
95 -> 97 ;
98 [label="gini = 0.765\nsamples = 14\nvalue = [0, 0, 2, 2, 5, 0, 0, 0, 3, 2]"] ;
97 -> 98 ;
99 [label="gini = 0.36\nsamples = 19\nvalue = [0, 0, 1, 0, 1, 0, 0, 2, 15, 0]"] ;
97 -> 99 ;
100 [label="X[18] <= 53.272\ngini = 0.763\nsamples = 157\nvalue = [3, 0, 14, 66, 14, 2, 2, 21, 15, 20]"] ;
94 -> 100 ;
101 [label="X[39] <= 81.466\ngini = 0.775\nsamples = 26\nvalue = [1, 0, 7, 1, 1, 1, 1, 0, 7, 7]"] ;
100 -> 101 ;
102 [label="gini = 0.463\nsamples = 11\nvalue = [0, 0, 4, 0, 0, 0, 0, 0, 7, 0]"] ;
101 -> 102 ;
103 [label="gini = 0.72\nsamples = 15\nvalue = [1, 0, 3, 1, 1, 1, 1, 0, 0, 7]"] ;
101 -> 103 ;
104 [label="X[17] <= 32.282\ngini = 0.701\nsamples = 131\nvalue = [2, 0, 7, 65, 13, 1, 1, 21, 8, 13]"] ;
100 -> 104 ;
105 [label="X[41] <= 0.655\ngini = 0.652\nsamples = 112\nvalue = [0, 0, 5, 62, 12, 1, 0, 11, 8, 13]"] ;
104 -> 105 ;
106 [label="X[4] <= 0.002\ngini = 0.786\nsamples = 23\nvalue = [0, 0, 0, 5, 7, 1, 0, 2, 3, 5]"] ;
105 -> 106 ;
107 [label="gini = 0.0\nsamples = 4\nvalue = [0, 0, 0, 0, 4, 0, 0, 0, 0, 0]"] ;
106 -> 107 ;
108 [label="gini = 0.798\nsamples = 19\nvalue = [0, 0, 0, 5, 3, 1, 0, 2, 3, 5]"] ;
106 -> 108 ;
109 [label="X[5] <= 23.418\ngini = 0.562\nsamples = 89\nvalue = [0, 0, 5, 57, 5, 0, 0, 9, 5, 8]"] ;
105 -> 109 ;
110 [label="X[15] <= -7.83\ngini = 0.713\nsamples = 51\nvalue = [0, 0, 3, 24, 4, 0, 0, 9, 4, 7]"] ;
109 -> 110 ;
111 [label="gini = 0.124\nsamples = 15\nvalue = [0, 0, 1, 14, 0, 0, 0, 0, 0, 0]"] ;
110 -> 111 ;
112 [label="X[0] <= 3541.513\ngini = 0.795\nsamples = 36\nvalue = [0, 0, 2, 10, 4, 0, 0, 9, 4, 7]"] ;
110 -> 112 ;
113 [label="X[26] <= 2.516\ngini = 0.795\nsamples = 32\nvalue = [0, 0, 2, 10, 4, 0, 0, 5, 4, 7]"] ;
112 -> 113 ;
114 [label="gini = 0.645\nsamples = 11\nvalue = [0, 0, 1, 1, 1, 0, 0, 0, 2, 6]"] ;
113 -> 114 ;
115 [label="X[19] <= 5.718\ngini = 0.726\nsamples = 21\nvalue = [0, 0, 1, 9, 3, 0, 0, 5, 2, 1]"] ;
113 -> 115 ;
116 [label="gini = 0.0\nsamples = 7\nvalue = [0, 0, 0, 7, 0, 0, 0, 0, 0, 0]"] ;
115 -> 116 ;
117 [label="gini = 0.776\nsamples = 14\nvalue = [0, 0, 1, 2, 3, 0, 0, 5, 2, 1]"] ;
115 -> 117 ;
118 [label="gini = 0.0\nsamples = 4\nvalue = [0, 0, 0, 0, 0, 0, 0, 4, 0, 0]"] ;
112 -> 118 ;
119 [label="X[29] <= 0.506\ngini = 0.241\nsamples = 38\nvalue = [0, 0, 2, 33, 1, 0, 0, 0, 1, 1]"] ;
109 -> 119 ;
120 [label="X[6] <= 21.369\ngini = 0.156\nsamples = 36\nvalue = [0, 0, 2, 33, 0, 0, 0, 0, 0, 1]"] ;
119 -> 120 ;
121 [label="gini = 0.444\nsamples = 3\nvalue = [0, 0, 2, 1, 0, 0, 0, 0, 0, 0]"] ;
120 -> 121 ;
122 [label="X[25] <= 4.125\ngini = 0.059\nsamples = 33\nvalue = [0, 0, 0, 32, 0, 0, 0, 0, 0, 1]"] ;
120 -> 122 ;
123 [label="gini = 0.0\nsamples = 32\nvalue = [0, 0, 0, 32, 0, 0, 0, 0, 0, 0]"] ;
122 -> 123 ;
124 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]"] ;
122 -> 124 ;
125 [label="gini = 0.5\nsamples = 2\nvalue = [0, 0, 0, 0, 1, 0, 0, 0, 1, 0]"] ;
119 -> 125 ;
126 [label="gini = 0.67\nsamples = 19\nvalue = [2, 0, 2, 3, 1, 0, 1, 10, 0, 0]"] ;
104 -> 126 ;
127 [label="X[11] <= -27.553\ngini = 0.8\nsamples = 96\nvalue = [20, 0, 3, 9, 13, 0, 30, 0, 5, 16]"] ;
89 -> 127 ;
128 [label="X[27] <= 0.356\ngini = 0.429\nsamples = 30\nvalue = [0, 0, 0, 1, 2, 0, 22, 0, 0, 5]"] ;
127 -> 128 ;
129 [label="gini = 0.32\nsamples = 5\nvalue = [0, 0, 0, 1, 0, 0, 0, 0, 0, 4]"] ;
128 -> 129 ;
130 [label="X[30] <= 0.084\ngini = 0.218\nsamples = 25\nvalue = [0, 0, 0, 0, 2, 0, 22, 0, 0, 1]"] ;
128 -> 130 ;
131 [label="X[32] <= 0.045\ngini = 0.153\nsamples = 24\nvalue = [0, 0, 0, 0, 2, 0, 22, 0, 0, 0]"] ;
130 -> 131 ;
132 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0]"] ;
131 -> 132 ;
133 [label="X[41] <= 0.784\ngini = 0.083\nsamples = 23\nvalue = [0, 0, 0, 0, 1, 0, 22, 0, 0, 0]"] ;
131 -> 133 ;
134 [label="gini = 0.0\nsamples = 21\nvalue = [0, 0, 0, 0, 0, 0, 21, 0, 0, 0]"] ;
133 -> 134 ;
135 [label="gini = 0.5\nsamples = 2\nvalue = [0, 0, 0, 0, 1, 0, 1, 0, 0, 0]"] ;
133 -> 135 ;
136 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]"] ;
130 -> 136 ;
137 [label="X[10] <= 270.408\ngini = 0.815\nsamples = 66\nvalue = [20, 0, 3, 8, 11, 0, 8, 0, 5, 11]"] ;
127 -> 137 ;
138 [label="gini = 0.0\nsamples = 9\nvalue = [9, 0, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
137 -> 138 ;
139 [label="X[10] <= 899.257\ngini = 0.838\nsamples = 57\nvalue = [11, 0, 3, 8, 11, 0, 8, 0, 5, 11]"] ;
137 -> 139 ;
140 [label="X[29] <= 0.463\ngini = 0.815\nsamples = 52\nvalue = [11, 0, 3, 8, 11, 0, 8, 0, 0, 11]"] ;
139 -> 140 ;
141 [label="X[19] <= 3.387\ngini = 0.788\nsamples = 40\nvalue = [11, 0, 3, 8, 3, 0, 4, 0, 0, 11]"] ;
140 -> 141 ;
142 [label="gini = 0.672\nsamples = 16\nvalue = [1, 0, 0, 8, 1, 0, 3, 0, 0, 3]"] ;
141 -> 142 ;
143 [label="X[5] <= 18.78\ngini = 0.691\nsamples = 24\nvalue = [10, 0, 3, 0, 2, 0, 1, 0, 0, 8]"] ;
141 -> 143 ;
144 [label="gini = 0.0\nsamples = 5\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 0, 5]"] ;
143 -> 144 ;
145 [label="gini = 0.659\nsamples = 19\nvalue = [10, 0, 3, 0, 2, 0, 1, 0, 0, 3]"] ;
143 -> 145 ;
146 [label="gini = 0.444\nsamples = 12\nvalue = [0, 0, 0, 0, 8, 0, 4, 0, 0, 0]"] ;
140 -> 146 ;
147 [label="gini = 0.0\nsamples = 5\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 5, 0]"] ;
139 -> 147 ;
148 [label="X[1] <= 6425.041\ngini = 0.656\nsamples = 128\nvalue = [7, 0, 0, 5, 46, 1, 2, 58, 9, 0]"] ;
14 -> 148 ;
149 [label="X[35] <= 0.331\ngini = 0.626\nsamples = 69\nvalue = [7, 0, 0, 5, 40, 1, 2, 8, 6, 0]"] ;
148 -> 149 ;
150 [label="gini = 0.742\nsamples = 19\nvalue = [3, 0, 0, 1, 1, 1, 1, 8, 4, 0]"] ;
149 -> 150 ;
151 [label="X[9] <= 116.612\ngini = 0.377\nsamples = 50\nvalue = [4, 0, 0, 4, 39, 0, 1, 0, 2, 0]"] ;
149 -> 151 ;
152 [label="X[41] <= 0.695\ngini = 0.301\nsamples = 47\nvalue = [1, 0, 0, 4, 39, 0, 1, 0, 2, 0]"] ;
151 -> 152 ;
153 [label="X[34] <= 0.051\ngini = 0.179\nsamples = 42\nvalue = [1, 0, 0, 1, 38, 0, 1, 0, 1, 0]"] ;
152 -> 153 ;
154 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]"] ;
153 -> 154 ;
155 [label="X[6] <= 15.523\ngini = 0.139\nsamples = 41\nvalue = [1, 0, 0, 1, 38, 0, 1, 0, 0, 0]"] ;
153 -> 155 ;
156 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
155 -> 156 ;
157 [label="X[24] <= 0.743\ngini = 0.096\nsamples = 40\nvalue = [0, 0, 0, 1, 38, 0, 1, 0, 0, 0]"] ;
155 -> 157 ;
158 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 1, 0, 0, 0]"] ;
157 -> 158 ;
159 [label="X[40] <= 207.05\ngini = 0.05\nsamples = 39\nvalue = [0, 0, 0, 1, 38, 0, 0, 0, 0, 0]"] ;
157 -> 159 ;
160 [label="gini = 0.0\nsamples = 37\nvalue = [0, 0, 0, 0, 37, 0, 0, 0, 0, 0]"] ;
159 -> 160 ;
161 [label="gini = 0.5\nsamples = 2\nvalue = [0, 0, 0, 1, 1, 0, 0, 0, 0, 0]"] ;
159 -> 161 ;
162 [label="gini = 0.56\nsamples = 5\nvalue = [0, 0, 0, 3, 1, 0, 0, 0, 1, 0]"] ;
152 -> 162 ;
163 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0, 0, 0, 0, 0, 0, 0, 0, 0]"] ;
151 -> 163 ;
164 [label="X[5] <= 26.587\ngini = 0.269\nsamples = 59\nvalue = [0, 0, 0, 0, 6, 0, 0, 50, 3, 0]"] ;
148 -> 164 ;
165 [label="X[9] <= 31.541\ngini = 0.169\nsamples = 55\nvalue = [0, 0, 0, 0, 2, 0, 0, 50, 3, 0]"] ;
164 -> 165 ;
166 [label="gini = 0.0\nsamples = 2\nvalue = [0, 0, 0, 0, 2, 0, 0, 0, 0, 0]"] ;
165 -> 166 ;
167 [label="X[35] <= 0.635\ngini = 0.107\nsamples = 53\nvalue = [0, 0, 0, 0, 0, 0, 0, 50, 3, 0]"] ;
165 -> 167 ;
168 [label="X[36] <= 0.129\ngini = 0.038\nsamples = 51\nvalue = [0, 0, 0, 0, 0, 0, 0, 50, 1, 0]"] ;
167 -> 168 ;
169 [label="gini = 0.0\nsamples = 50\nvalue = [0, 0, 0, 0, 0, 0, 0, 50, 0, 0]"] ;
168 -> 169 ;
170 [label="gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]"] ;
168 -> 170 ;
171 [label="gini = 0.0\nsamples = 2\nvalue = [0, 0, 0, 0, 0, 0, 0, 0, 2, 0]"] ;
167 -> 171 ;
172 [label="gini = 0.0\nsamples = 4\nvalue = [0, 0, 0, 0, 4, 0, 0, 0, 0, 0]"] ;
164 -> 172 ;
}
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