{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import KMeans\nimport itertools\nfrom sklearn.mixture import BayesianGaussianMixture","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"notebook copied from https://www.kaggle.com/code/leandrodestefani/tps-jul-22-gaussianmixture-gbm and edited by me","metadata":{}},{"cell_type":"markdown","source":"# read data","metadata":{}},{"cell_type":"code","source":"train=pd.read_csv(\"../input/tabular-playground-series-jul-2022/data.csv\")\nsubmission=pd.read_csv(\"../input/tabular-playground-series-jul-2022/sample_submission.csv\")\nprint('train data shape is :',train.shape)\nprint('submission data shape is :',submission.shape)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA","metadata":{}},{"cell_type":"code","source":"train.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.dtypes","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.describe(include='all').T","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"a=[col for col in train.columns if train[col].isnull().any()]\nprint(a)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train[train.iloc[:,1:].duplicated()]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**check data distribution**","metadata":{}},{"cell_type":"markdown","source":"# Histplot \n\nA histogram is a classic visualization tool that represents the distribution of one or more variables by counting the number of observations that fall within disrete bins.\n\nThis function can normalize the statistic computed within each bin to estimate frequency, density or probability mass, and it can add a smooth curve obtained using a kernel density estimate, similar to kdeplot().\n\n","metadata":{}},{"cell_type":"code","source":"col_list=train.columns\nncols = 5\nnrows = 6\n\nfig, axes = plt.subplots(nrows, ncols, figsize=(30,30), facecolor='#EAEAF2')\n\nfor r in range(nrows):\n    for c in range(ncols):\n        col = col_list[r*ncols+c]\n        sns.histplot(x=train[col], ax=axes[r, c])\n        axes[r, c].set_ylabel('')\n        axes[r, c].set_xlabel(col, fontsize=15, fontweight='bold')\n        axes[r, c].tick_params(labelsize=10, width=0.5)\n        axes[r, c].xaxis.offsetText.set_fontsize(20)\n        axes[r, c].yaxis.offsetText.set_fontsize(20)\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**t single correlation heatmap**","metadata":{}},{"cell_type":"code","source":"sns.set(rc = {'figure.figsize':(13,8)})\ndf_corr=train.iloc[:,1:].corr()\nsns.heatmap(df_corr, vmax=1, vmin=-1, center=0)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# prepare training data","metadata":{}},{"cell_type":"markdown","source":"\n# FastICA:\na fast algorithm for Independent Component Analysis.\nIndependent component analysis separates a multivariate signal into additive subcomponents that are maximally independent.\nIt is implemented in scikit-learn using the Fast ICA algorithm.\nTypically, ICA is not used for reducing dimensionality but for separating superimposed signals.\nSince the ICA model does not include a noise term, for the model to be correct, whitening must be applied. \nThis can be done internally using the whiten argument or manually using one of the PCA variants.\n\nIt is classically used to separate mixed signals (a problem known as blind source separation), as in the example below:\n\n\n![image.png](attachment:859ba26c-5224-4f81-8568-ec199c3a8f5e.png)\n","metadata":{},"attachments":{"859ba26c-5224-4f81-8568-ec199c3a8f5e.png":{"image/png":"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"}}},{"cell_type":"code","source":"from sklearn.decomposition import FastICA\n\nica = FastICA(n_components=2)\nS_1 = ica.fit_transform(train.iloc[:,8:15])\nS_2 = ica.fit_transform(train.iloc[:,23:])\nS_3 = ica.fit_transform(train.iloc[:,1:8])\nS_4 = ica.fit_transform(train.iloc[:,15:23])\n\ncomp_df_1=pd.DataFrame(S_1,columns=['01','02'])\ncomp_df_2=pd.DataFrame(S_2,columns=['03','04'])\ncomp_df_3=pd.DataFrame(S_3,columns=['05','06'])\ncomp_df_4=pd.DataFrame(S_4,columns=['07','08'])\ncomp_df_1.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data=pd.concat([train,comp_df_1,comp_df_2,comp_df_3,comp_df_4],axis=1)\ntrain_data.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**standardization**","metadata":{}},{"cell_type":"markdown","source":"# StandardScaler\nStandardize features by removing the mean and scaling to unit variance.\n\nThe standard score of a sample x is calculated as:\n\n    z = (x - u) / s\n\nwhere u is the mean of the training samples or zero if with_mean=False, and s is the standard deviation of the training samples or one if with_std=False.\n\nCentering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using :meth:transform.\n\nStandardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).\n\nFor instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.\n\n\n","metadata":{}},{"cell_type":"code","source":"train_data=train_data.iloc[:,1:]\n\nscaler = StandardScaler()\nscaler.fit(train_data)\nX=pd.DataFrame(scaler.transform(train_data),columns=train_data.columns)\nX.describe().T","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**make column list**","metadata":{}},{"cell_type":"code","source":"train_data.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"order_list_=train_data.iloc[:,29:].columns\norder_list_=order_list_.append(train_data.iloc[:,7:14].columns)\norder_list_=order_list_.append(train_data.iloc[:,22:29].columns)\norder_list_=order_list_.append(train_data.iloc[:,0:7].columns)\norder_list_=order_list_.append(train_data.iloc[:,14:22].columns)\nprint(len(order_list_))\norder_list_","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**choose columns**","metadata":{}},{"cell_type":"markdown","source":"# K-means\nThe KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares (see below). This algorithm requires the number of clusters to be specified. It scales well to large number of samples and has been used across a large range of application areas in many different fields.\n\n**Variance** measures how far each number in the set is from the mean (average), and thus from every other number in the set.","metadata":{}},{"cell_type":"code","source":"test_list=[]\nbest_score=0\nfor col in order_list_.tolist():  \n    test_list.append(col)\n    X_test=X[test_list]\n    cls = KMeans(n_clusters=8)\n    result = cls.fit(X_test)\n    result_list=result.cluster_centers_\n    a=list(itertools.combinations(result_list, 2))\n    diff_sum=0\n    for list_ in list(itertools.combinations(result_list, 2)):\n        diff_sum=diff_sum+np.abs(np.linalg.norm(list_[0]-list_[1]))\n    print('score is : ',diff_sum/28)\n    if best_score<diff_sum/28:\n        best_score=diff_sum/28\n    else:\n        test_list.pop()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# BayesianGaussianMixture\nThis class allows to infer an approximate posterior distribution over the parameters of a Gaussian mixture distribution. The effective number of components can be inferred from the data.\n\nThis class implements two types of prior for the weights distribution: a finite mixture model with Dirichlet distribution and an infinite mixture model with the Dirichlet Process. In practice Dirichlet Process inference algorithm is approximated and uses a truncated distribution with a fixed maximum number of components (called the Stick-breaking representation). The number of components actually used almost always depends on the data.","metadata":{}},{"cell_type":"code","source":"print(len(test_list))\nX_test=X[test_list]\ncls_gaussian= BayesianGaussianMixture(n_components=8,\n                             max_iter=1000)\nsubmission['Predicted']=cls_gaussian.fit_predict(X_test)\nsubmission","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# create submission file","metadata":{}},{"cell_type":"code","source":"submission.to_csv('submission.csv',index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}