[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:from sklearn.decomposition import PCA\nfrom sklearn import preprocessing\nfrom sklearn import metrics\nfrom sklearn.manifold import MDS\ndef clusterDocuments(X, scaled = False, PCA_X = False, n_clusters = 8,\nmethod = \'KMeans\', visualize = \'PCA\', verbose = True, n_labels=3):\nif scaled:\nX = preprocessing.scale(X)\nif PCA_X:\nX = PCA().fit_transform(X)\nif PCA_X:\nX_2D_PCA = X[:, 0:2]\nelse:\nX_2D_PCA = PCA(n_components=2).fit_transform(X)\nX_2D_MDS = MDS(n_components=2).fit_transform(X)\nlabels = getLabels(X, n_clusters, method)\nif visualize == \'PCA\':\nprint2D(X_2D_PCA, \'PCA 1st component\', \'PCA 2nd component\', method, labels, n_labels)\nelif visualize == \'MDS\':\nprint2D(X_2D_MDS, \'MDS 1st component\', \'MDS 2nd component\', method, labels, n_labels)\nprint(\'Number of clusters: \', len(set(labels)))\nprint("Silhouette Coefficient: %0.3f"\n% metrics.silhouette_score(X, labels, random_state=randomState))\nif verbose:\nprintClusters(labels)\ndef visualizeCovariance(X, scaled = False):\nif scaled:\nX = preprocessing.scale(X)\ntitle = \'Standardized\'\nelse:\ntitle = \'Non-Standardized\'\npca = PCA(n_components=None)\npca.fit(X)\ncovariance = pca.get_covariance()\nwidth = 0.5\nind = np.arange(len(pca.explained_variance_ratio_))\nplt.figure()\nax = plt.gca()\nax.bar(ind, pca.explained_variance_ratio_, width, color=\'gray\')\nplt.ylabel(\'Explained Variance Ratio\')\nplt.xlabel(\'Components\')\nplt.title(title, fontweight=\'bold\')\nplt.show()\nfig, ax = plt.subplots()\nheatmap = ax.pcolor(covariance, cmap=plt.cm.Blues, alpha=0.8)\nfig = plt.gcf()\nfig.set_size_inches(8, 8)\nax.set_frame_on(False)\nax.set_yticks(np.arange(covariance.shape[0]) + 0.5, minor=False)\nax.set_xticks(np.arange(covariance.shape[1]) + 0.5, minor=False)\nax.invert_yaxis()\nax.xaxis.tick_top()\nlabels = df_all.columns\nax.set_xticklabels(labels, minor=False)\nax.set_yticklabels(labels, minor=False)\nplt.xticks(rotation=90)\nax.grid(False)\nax = plt.gca()\nfor t in ax.xaxis.get_major_ticks():\nt.tick1On = False\nt.tick2On = False\nfor t in ax.yaxis.get_major_ticks():\nt.tick1On = False\nt.tick2On = False\nplt.title(title, fontweight=\'bold\', y=0)\nplt.show()'}, {'role': 'assistant', 'content': "['data_exploration', 'modelling']"}]